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Białas M, Mirończuk MM, Mańdziuk J. Leveraging spiking neural networks for topic modeling. Neural Netw 2024; 178:106494. [PMID: 38972130 DOI: 10.1016/j.neunet.2024.106494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/06/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Abstract
This article investigates the application of spiking neural networks (SNNs) to the problem of topic modeling (TM): the identification of significant groups of words that represent human-understandable topics in large sets of documents. Our research is based on the hypothesis that an SNN that implements the Hebbian learning paradigm is capable of becoming specialized in the detection of statistically significant word patterns in the presence of adequately tailored sequential input. To support this hypothesis, we propose a novel spiking topic model (STM) that transforms text into a sequence of spikes and uses that sequence to train single-layer SNNs. In STM, each SNN neuron represents one topic, and each of the neuron's weights corresponds to one word. STM synaptic connections are modified according to spike-timing-dependent plasticity; after training, the neurons' strongest weights are interpreted as the words that represent topics. We compare the performance of STM with four other TM methods Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Embedding Topic Model (ETM) and BERTopic on three datasets: 20Newsgroups, BBC news, and AG news. The results demonstrate that STM can discover high-quality topics and successfully compete with comparative classical methods. This sheds new light on the possibility of the adaptation of SNN models in unsupervised natural language processing.
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Affiliation(s)
- Marcin Białas
- National Information Processing Institute, al. Niepodległości 188b, 00-608, Warsaw, Poland.
| | | | - Jacek Mańdziuk
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland.
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2
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Grimaldi A, Boutin V, Ieng SH, Benosman R, Perrinet LU. A robust event-driven approach to always-on object recognition. Neural Netw 2024; 178:106415. [PMID: 38852508 DOI: 10.1016/j.neunet.2024.106415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 04/05/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024]
Abstract
We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events captured by a neuromorphic camera, these time surfaces allow to encode the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we have extended this method to improve its performance. First, we add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns (Grimaldi et al., 2021). We also provide a new mathematical formalism that allows an analogy to be drawn between the HOTS algorithm and Spiking Neural Networks (SNN). Following this analogy, we transform the offline pattern categorization method into an online and event-driven layer. This classifier uses the spiking output of the network to define new time surfaces and we then perform the online classification with a neuromimetic implementation of a multinomial logistic regression. These improvements not only consistently increase the performance of the network, but also bring this event-driven pattern recognition algorithm fully online. The results have been validated on different datasets: Poker-DVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST (Orchard, Jayawant et al., 2015) and DVS Gesture (Amir et al., 2017). This demonstrates the efficiency of this bio-realistic SNN for ultra-fast object recognition through an event-by-event categorization process.
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Affiliation(s)
- Antoine Grimaldi
- Aix-Marseille Universit, Institut de Neurosciences de la Timone, CNRS, Marseille, France.
| | - Victor Boutin
- Carney Institute for Brain Science, Brown University, Providence, RI, United States; Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, Toulouse, France.
| | - Sio-Hoi Ieng
- Institut de la Vision, Sorbonne Université, CNRS, Paris, France.
| | - Ryad Benosman
- Robotics Institute, Carnegie Mellon University, Pittsburg, PA, United States.
| | - Laurent U Perrinet
- Aix-Marseille Universit, Institut de Neurosciences de la Timone, CNRS, Marseille, France.
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3
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Liu R, Liu T, Liu W, Luo B, Li Y, Fan X, Zhang X, Cui W, Teng Y. SemiSynBio: A new era for neuromorphic computing. Synth Syst Biotechnol 2024; 9:594-599. [PMID: 38711551 PMCID: PMC11070324 DOI: 10.1016/j.synbio.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.
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Affiliation(s)
- Ruicun Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Wuge Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Boyu Luo
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Yuchen Li
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Xinyue Fan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Xianchao Zhang
- Institute of Information Network and Artificial Intelligence, Jiaxing University, Jiaxing, 314001, China
| | - Wei Cui
- South China University of Technology, Guangzhou, 510641, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
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4
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Jannesar N, Akbarzadeh-Sherbaf K, Safari S, Vahabie AH. SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach. Neural Netw 2024; 177:106368. [PMID: 38761415 DOI: 10.1016/j.neunet.2024.106368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 03/28/2024] [Accepted: 05/05/2024] [Indexed: 05/20/2024]
Abstract
The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.
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Affiliation(s)
- Nastaran Jannesar
- High Performance Embedded Architecture Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | | | - Saeed Safari
- High Performance Embedded Architecture Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Abdol-Hossein Vahabie
- Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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5
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Kumar A, Das D, Lin DJX, Huang L, Yap SLK, Tan HK, Lim RJJ, Tan HR, Toh YT, Lim ST, Fong X, Ho P. Bimodal alteration of cognitive accuracy for spintronic artificial neural networks. NANOSCALE HORIZONS 2024; 9:1522-1531. [PMID: 38954430 DOI: 10.1039/d4nh00097h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Spintronics-based artificial neural networks (ANNs) exhibiting nonvolatile, fast, and energy-efficient computing capabilities are promising neuromorphic hardware for performing complex cognitive tasks of artificial intelligence and machine learning. Early experimental efforts focused on multistate device concepts to enhance synaptic weight precisions, albeit compromising on cognitive accuracy due to their low magnetoresistance. Here, we propose a hybrid approach based on the tuning of tunnel magnetoresistance (TMR) and the number of states in the compound magnetic tunnel junctions (MTJs) to improve the cognitive performance of an all-spin ANN. A TMR variation of 33-78% is controlled by the free layer (FL) thickness wedge (1.6-2.6 nm) across the wafer. Meanwhile, the number of resistance states in the compound MTJ is manipulated by varying the number of constituent MTJ cells (n = 1-3), generating n + 1 states with a TMR difference between consecutive states of at least 21%. Using MNIST handwritten digit and fashion object databases, the test accuracy of the compound MTJ ANN is observed to increase with the number of intermediate states for a fixed FL thickness or TMR. Meanwhile, the test accuracy for a 1-cell MTJ increases linearly by 8.3% and 7.4% for handwritten digits and fashion objects, respectively, with increasing TMR. Interestingly, a multifarious TMR dependence of test accuracy is observed with the increasing synaptic complexity in the 2- and 3-cell MTJs. By leveraging on the bimodal tuning of multilevel and TMR, we establish viable paths for enhancing the cognitive performance of spintronic ANN for in-memory and neuromorphic computing.
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Affiliation(s)
- Anuj Kumar
- Physics Department, National University of Singapore, Singapore, 117551, Singapore
| | - Debasis Das
- Electrical and Computer Engineering Department, National University of Singapore, Singapore, 117583, Singapore.
| | - Dennis J X Lin
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Lisen Huang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Sherry L K Yap
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Hang Khume Tan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Royston J J Lim
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Hui Ru Tan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Yeow Teck Toh
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Sze Ter Lim
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
| | - Xuanyao Fong
- Electrical and Computer Engineering Department, National University of Singapore, Singapore, 117583, Singapore.
| | - Pin Ho
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore, 138634, Singapore.
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6
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Zhang YH, Sipling C, Qiu E, Schuller IK, Di Ventra M. Collective dynamics and long-range order in thermal neuristor networks. Nat Commun 2024; 15:6986. [PMID: 39143044 PMCID: PMC11324871 DOI: 10.1038/s41467-024-51254-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 08/04/2024] [Indexed: 08/16/2024] Open
Abstract
In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.
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Affiliation(s)
- Yuan-Hang Zhang
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Chesson Sipling
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Erbin Qiu
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
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7
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Li H, Li Q, Sun T, Zhou Y, Han ST. Recent advances in artificial neuromorphic applications based on perovskite composites. MATERIALS HORIZONS 2024. [PMID: 39140168 DOI: 10.1039/d4mh00574k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.
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Affiliation(s)
- Huaxin Li
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Qingxiu Li
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Tao Sun
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen 518060, P. R. China
| | - Su-Ting Han
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, P. R. China.
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8
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Esfahani S, Cotrufo M, Alù A. Tailoring Space-Time Nonlocality for Event-Based Image Processing Metasurfaces. PHYSICAL REVIEW LETTERS 2024; 133:063801. [PMID: 39178445 DOI: 10.1103/physrevlett.133.063801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 06/27/2024] [Indexed: 08/25/2024]
Abstract
Analog computation with passive optical components can enhance processing speeds and reduce power consumption, recently attracting renewed interest thanks to the opportunities enabled by metasurfaces. Basic image processing tasks, such as spatial differentiation, have been recently demonstrated based on engineered nonlocalities in metasurfaces, but next-generation computational schemes require more advanced capabilities. Here, by simultaneously tailoring the nonlocal electromagnetic response of a metasurface in space and time, we demonstrate a passive ultrathin silicon-based device that performs mixed spatiotemporal differentiation of input images, realizing event-based edge detection. The metasurface performs spatial differentiation only when the input image is evolving in time, resulting in spatiotemporal image processing on subpicosecond timescales. Moreover, the metasurface design can be tailored to selectively enhance objects moving at desired speeds. Our results point towards fully passive processing of spatiotemporal signals, for highly compact neuromorphic cameras.
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Geng X, Gao Q, Wu G, Huang J, Wang G, Xin Y, Gao J, Liang B, Gao L, Wang M, Xiao Z, Chu PK, Huang A. Stable and Tunable Quantum Conductance in Spider-Silk-like Synaptic Device for Neurocomputing. ACS APPLIED MATERIALS & INTERFACES 2024; 16:39807-39817. [PMID: 39011905 DOI: 10.1021/acsami.4c06328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
The quantum conductance (QC) behaviors in synaptic devices with stable and tunable conductance states are essential for high-density storage and brain-like neurocomputing (NC). In this work, inspired by the discontinuous transport of fluid in spider silk, a synaptic device composed of a silicon oxide nanowire network embedded with silicon quantum dots (Si-QDs@SiOx) is designed. The tunable QC behaviors are achieved in both the SET and RESET processes, and the QC states exhibit stable retention time exceeding 104 s in the synaptic device and show stable reproducibility after an interval of two months. The synaptic plasticity, including long-term potentiation/depression and Pavlovian conditioning function, is simulated based on the tunable conductance. The mechanism of stable and tunable QC behaviors is analyzed and clarified by beading effect of spider silk in Si-QDs@SiOx nanowires structure. The digit recognition capability of the device is evaluated by simulation using an artificial neural network consisting of the Si-QDs@SiOx-based synaptic device. These results provide insights into the development of neurocomputing systems with high classification accuracy.
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Affiliation(s)
- Xueli Geng
- School of Physics, Beihang University, Beijing 100191, China
| | - Qin Gao
- Key Laboratory of Bio-Inspired Smart Interfacial Science and Technology of Ministry of Education, School of Chemistry, and School of Physics, Beihang University, Beijing 100191, China
| | - Gang Wu
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Jiangshun Huang
- School of Physics, Beihang University, Beijing 100191, China
| | - Guoxing Wang
- School of Physics, Beihang University, Beijing 100191, China
| | - Yanbo Xin
- School of Physics, Beihang University, Beijing 100191, China
| | - Juan Gao
- School of Physics, Beihang University, Beijing 100191, China
| | - Bo Liang
- School of Physics, Beihang University, Beijing 100191, China
| | - Lei Gao
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Mei Wang
- School of Physics, Beihang University, Beijing 100191, China
| | - Zhisong Xiao
- Beijing Information Science & Technology University, Beijing, Beihang University, Beijing 100191, China
| | - Paul K Chu
- Department of Physics, Department of Materials Science and Engineering, and Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon 999077, Hong Kong, China
| | - Anping Huang
- School of Physics, Beihang University, Beijing 100191, China
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10
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Gu S, Mattar MG, Tang H, Pan G. Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection. SCIENCE ADVANCES 2024; 10:eadm8430. [PMID: 39058783 PMCID: PMC11277393 DOI: 10.1126/sciadv.adm8430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.
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Affiliation(s)
- Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Marcelo G. Mattar
- Department of Psychology, New York University, New York, NY 10003, USA
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
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11
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Putra RVW, Marchisio A, Shafique M. SNN4Agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents. Front Robot AI 2024; 11:1401677. [PMID: 39131197 PMCID: PMC11309964 DOI: 10.3389/frobt.2024.1401677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 06/14/2024] [Indexed: 08/13/2024] Open
Abstract
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically powered by portable batteries, they require extremely low power/energy consumption to operate in a long lifespan. To solve this challenge, neuromorphic computing has emerged as a promising solution, where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based cameras or data conversion pre-processing to perform sparse computations efficiently. However, the studies of SNN deployments for autonomous agents are still at an early stage. Hence, the optimization stages for enabling efficient embodied SNN deployments for autonomous agents have not been defined systematically. Toward this, we propose a novel framework called SNN4Agents that consists of a set of optimization techniques for designing energy-efficient embodied SNNs targeting autonomous agent applications. Our SNN4Agents employs weight quantization, timestep reduction, and attention window reduction to jointly improve the energy efficiency, reduce the memory footprint, optimize the processing latency, while maintaining high accuracy. In the evaluation, we investigate use cases of event-based car recognition, and explore the trade-offs among accuracy, latency, memory, and energy consumption. The experimental results show that our proposed framework can maintain high accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement as compared to the state-of-the-art work for the NCARS dataset. In this manner, our SNN4Agents framework paves the way toward enabling energy-efficient embodied SNN deployments for autonomous agents.
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12
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Laswick Z, Wu X, Surendran A, Zhou Z, Ji X, Matrone GM, Leong WL, Rivnay J. Tunable anti-ambipolar vertical bilayer organic electrochemical transistor enable neuromorphic retinal pathway. Nat Commun 2024; 15:6309. [PMID: 39060249 PMCID: PMC11282299 DOI: 10.1038/s41467-024-50496-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Increasing demand for bio-interfaced human-machine interfaces propels the development of organic neuromorphic electronics with small form factors leveraging both ionic and electronic processes. Ion-based organic electrochemical transistors (OECTs) showing anti-ambipolarity (OFF-ON-OFF states) reduce the complexity and size of bio-realistic Hodgkin-Huxley(HH) spiking circuits and logic circuits. However, limited stable anti-ambipolar organic materials prevent the design of integrated, tunable, and multifunctional neuromorphic and logic-based systems. In this work, a general approach for tuning anti-ambipolar characteristics is presented through assembly of a p-n bilayer in a vertical OECT (vOECT) architecture. The vertical OECT design reduces device footprint, while the bilayer material tuning controls the anti-ambipolarity characteristics, allowing control of the device's on and off threshold voltages, and peak position, while reducing size thereby enabling tunable threshold spiking neurons and logic gates. Combining these components, a mimic of the retinal pathway reproducing the wavelength and light intensity encoding of horizontal cells to spiking retinal ganglion cells is demonstrated. This work enables further incorporation of conformable and adaptive OECT electronics into biointegrated devices featuring sensory coding through parallel processing for diverse artificial intelligence and computing applications.
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Affiliation(s)
- Zachary Laswick
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Xihu Wu
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Abhijith Surendran
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | - Zhongliang Zhou
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Xudong Ji
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA
| | | | - Wei Lin Leong
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
| | - Jonathan Rivnay
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA.
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA.
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13
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Rajib MM, Bindal N, Raj RK, Kaushik BK, Atulasimha J. Skyrmion-mediated nonvolatile ternary memory. Sci Rep 2024; 14:17199. [PMID: 39060298 PMCID: PMC11282189 DOI: 10.1038/s41598-024-66853-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {- 1, 0, + 1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~ 2 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~ 104X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation.
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Affiliation(s)
- Md Mahadi Rajib
- Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Namita Bindal
- Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
- Department of Electronics and Communication Engineering, MVJ College of Engineering, Bangalore, 560067, India
| | - Ravish Kumar Raj
- Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Brajesh Kumar Kaushik
- Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India
| | - Jayasimha Atulasimha
- Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA.
- Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA.
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14
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Tenzin S, Rassau A, Chai D. Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey. Biomimetics (Basel) 2024; 9:444. [PMID: 39056885 PMCID: PMC11274992 DOI: 10.3390/biomimetics9070444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024] Open
Abstract
Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.
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Affiliation(s)
| | - Alexander Rassau
- School of Engineering, Edith Cowan University, Perth, WA 6027, Australia; (S.T.); (D.C.)
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15
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Fernández JG, Keemink S, van Gerven M. Gradient-free training of recurrent neural networks using random perturbations. Front Neurosci 2024; 18:1439155. [PMID: 39050673 PMCID: PMC11267880 DOI: 10.3389/fnins.2024.1439155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024] Open
Abstract
Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.
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Affiliation(s)
- Jesús García Fernández
- Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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16
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Hamburg S, Jimenez Rodriguez A, Htet A, Di Nuovo A. Active Inference for Learning and Development in Embodied Neuromorphic Agents. ENTROPY (BASEL, SWITZERLAND) 2024; 26:582. [PMID: 39056944 PMCID: PMC11276484 DOI: 10.3390/e26070582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/23/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.
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Affiliation(s)
- Sarah Hamburg
- Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK; (A.J.R.); (A.H.); (A.D.N.)
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17
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Soleimani A, Forooraghi K, Atlasbaf Z. Phasor-based analysis of a neuromorphic architecture for microwave sensing. Sci Rep 2024; 14:15590. [PMID: 38971856 PMCID: PMC11227532 DOI: 10.1038/s41598-024-66156-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 06/27/2024] [Indexed: 07/08/2024] Open
Abstract
This article presents a design procedure for implementing artificial neural networks (ANNs) using conventional microwave components at the hardware level with potential applications in radar and remote sensing. The main objective is to develop structured hardware design methods for implementing artificial neurons, utilizing microwave devices to create neuromorphic devices compatible with high-frequency electromagnetic waves. The research aims to address the challenge of encoding and modulating information in electromagnetic waves into a format suitable for the neuromorphic device by using frequency-modulated information instead of intensity-modulated information. It also proposes a method for integrating principal component analysis as a dimensionality reduction technique with the implementation of ANNs on a single hardware. As a dummy task, the process outlined here is used to implement an artificial neural network at the hardware level, with a specific emphasis on creating hardware that is capable of performing matrix multiplications in the form of dot products while also being able to extract the resulting data in an interpretable manner. The proposed implementation involves the use of directional couplers to implement weights and sample the resulting signal at specific intervals to obtain the multiplication result.
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Affiliation(s)
- Ashkan Soleimani
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, 14115-194, Iran
| | - Keyvan Forooraghi
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, 14115-194, Iran.
| | - Zahra Atlasbaf
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, 14115-194, Iran
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18
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Baltussen MG, de Jong TJ, Duez Q, Robinson WE, Huck WTS. Chemical reservoir computation in a self-organizing reaction network. Nature 2024; 631:549-555. [PMID: 38926572 PMCID: PMC11254755 DOI: 10.1038/s41586-024-07567-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 05/14/2024] [Indexed: 06/28/2024]
Abstract
Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems.
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Affiliation(s)
- Mathieu G Baltussen
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Thijs J de Jong
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Quentin Duez
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - William E Robinson
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Wilhelm T S Huck
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands.
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19
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Zeng T, Shi S, Hu K, Jia L, Li B, Sun K, Su H, Gu Y, Xu X, Song D, Yan X, Chen J. Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401021. [PMID: 38695721 DOI: 10.1002/adma.202401021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/29/2024] [Indexed: 05/15/2024]
Abstract
Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.
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Affiliation(s)
- Tao Zeng
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Shu Shi
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Kejun Hu
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Lanxin Jia
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Boyu Li
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Kaixuan Sun
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Hanxin Su
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Youdi Gu
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Xiaohong Xu
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Dongsheng Song
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Xiaobing Yan
- College of Electron and Information Engineering, Hebei University, Baoding, 071002, China
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- Suzhou Research Institute, National University of Singapore, Jiang Su, 215123, China
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20
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Kim DH, Cheong WH, Song H, Jeon JB, Kim G, Kim KM. Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design. MATERIALS HORIZONS 2024. [PMID: 38916265 DOI: 10.1039/d3mh02049e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Device and algorithm co-design aims to develop energy-efficient hardware that directly implements complex algorithms and optimizes algorithms to match the hardware's characteristics. Specifically, neuromorphic computing algorithms are constantly growing in complexity, necessitating an ongoing search for hardware implementations capable of handling these intricate algorithms. Here, we present a memristive Monte Carlo DropConnect (MC-DC) crossbar array developed through a hardware algorithm co-design approach. To implement the MC-DC neural network, stochastic switching and analog memory characteristics are required, and we achieved them using Ag-based diffusive selectors and Ru-based electrochemical metalization (ECM) memristors, respectively. The devices were integrated with a one-selector one-memristor (1S1M) structure, and their well-matched operating voltages and currents enabled stochastic readout and deterministic analog programming. With the integrated hardware, we successfully demonstrated the MC-DC operation. Additionally, the selector allowed for the control of switching polarity, and by understanding this hardware characteristic, we were able to modify the algorithm to fit it and further improve the network performance.
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Affiliation(s)
- Do Hoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Woon Hyung Cheong
- Applied Science Research Institute, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Jae Bum Jeon
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Geunyoung Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
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21
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Li R, Gong Y, Huang H, Zhou Y, Mao S, Wei Z, Zhang Z. Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2312825. [PMID: 39011981 DOI: 10.1002/adma.202312825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 06/12/2024] [Indexed: 07/17/2024]
Abstract
In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeable future.
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Affiliation(s)
- Renjie Li
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Yuanhao Gong
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Hai Huang
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Yuze Zhou
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Sixuan Mao
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
| | - Zhijian Wei
- SONT Technologies Co. LTD, Shenzhen, Guangdong, 510245, China
| | - Zhaoyu Zhang
- School of Science and Engineering, Guangdong Key Laboratory of Optoelectronic Materials and Chips, Shenzhen Key Lab of Semiconductor Lasers, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, 518172, China
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22
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Irastorza-Valera L, Soria-Gómez E, Benitez JM, Montáns FJ, Saucedo-Mora L. Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM). Biomimetics (Basel) 2024; 9:362. [PMID: 38921242 PMCID: PMC11202129 DOI: 10.3390/biomimetics9060362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.
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Affiliation(s)
- Luis Irastorza-Valera
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- PIMM Laboratory, ENSAM–Arts et Métiers ParisTech, 151 Bd de l’Hôpital, 75013 Paris, France
| | - Edgar Soria-Gómez
- Achúcarro Basque Center for Neuroscience, Barrio Sarriena, s/n, 48940 Leioa, Spain;
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain
- Department of Neurosciences, University of the Basque Country UPV/EHU, Barrio Sarriena, s/n, 48940 Leioa, Spain
| | - José María Benitez
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
| | - Francisco J. Montáns
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Mechanical and Aerospace Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Luis Saucedo-Mora
- E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain; (L.I.-V.); (J.M.B.); (F.J.M.)
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Ave, Cambridge, MA 02139, USA
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23
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Zhang Z, Sabbagh B, Chen Y, Yossifon G. Geometrically Scalable Iontronic Memristors: Employing Bipolar Polyelectrolyte Gels for Neuromorphic Systems. ACS NANO 2024; 18:15025-15034. [PMID: 38804641 PMCID: PMC11171754 DOI: 10.1021/acsnano.4c01730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/04/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024]
Abstract
Iontronics that are capable of mimicking the functionality of biological systems within an artificial fluidic network have long been pursued for biomedical applications and ion-based intelligence systems. Here, we report on facile and robust realization of iontronic bipolar memristors featuring a three-layer polyelectrolyte gel structure. Significant memristive hysteresis of ion currents was successfully accomplished, and the memory time proved geometrically scalable from 200 to 4000 s. These characteristics were enabled by the ion concentration polarization-induced rectification ratio within the polyelectrolyte gels. The memristors exhibited memory dynamics akin to those observed in unipolar devices, while the bipolar structure notably enabled prolonged memory time and enhanced the ion conductance switching ratio with mesoscale (10-1000 μm) geometry precision. These properties endow the devices with the capability of effective neuromorphic processing with pulse-based input voltage signals. Owing to their simple fabrication process and superior memristive performance, the presented iontronic bipolar memristors are versatile and can be easily integrated into small-scale iontronic circuits, thereby facilitating advanced neuromorphic computing functionalities.
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Affiliation(s)
- Zhenyu Zhang
- School
of Mechanical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Jiangsu
Key Laboratory for Design and Manufacture of Micro-Nano Biomedical
Instruments, School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Barak Sabbagh
- School
of Mechanical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Faculty
of Mechanical Engineering, Technion−Israel
Institute of Technology, Haifa 3200003, Israel
| | - Yunfei Chen
- Jiangsu
Key Laboratory for Design and Manufacture of Micro-Nano Biomedical
Instruments, School of Mechanical Engineering, Southeast University, Nanjing 211189, China
| | - Gilad Yossifon
- School
of Mechanical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- Department
of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
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24
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Ratwatte A, Somathilaka S, Balasubramaniam S, Gilad AA. Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network. BIOPHYSICAL REPORTS 2024; 4:100158. [PMID: 38848994 PMCID: PMC11231448 DOI: 10.1016/j.bpr.2024.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/20/2024] [Accepted: 05/31/2024] [Indexed: 06/09/2024]
Abstract
The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.
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Affiliation(s)
- Adrian Ratwatte
- School of Computing, University of Nebraska-Lincoln, 104 Schorr Center, Lincoln, Nebraska, USA.
| | - Samitha Somathilaka
- School of Computing, University of Nebraska-Lincoln, 104 Schorr Center, Lincoln, Nebraska, USA; VistaMilk Research Centre, Walton Institute for Information and Communication Systems Science, South East Technological University, Waterford, Ireland
| | | | - Assaf A Gilad
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, USA; Department of Radiology, Michigan State University, East Lansing, Michigan, USA
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25
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Scharf C, Witkowski O. Rebuilding the Habitable Zone from the Bottom up with Computational Zones. ASTROBIOLOGY 2024; 24:613-627. [PMID: 38853680 DOI: 10.1089/ast.2023.0035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Computation, if treated as a set of physical processes that act on information represented by states of matter, encompasses biological systems, digital systems, and other constructs and may be a fundamental measure of living systems. The opportunity for biological computation, represented in the propagation and selection-driven evolution of information-carrying organic molecular structures, has been partially characterized in terms of planetary habitable zones (HZs) based on primary conditions such as temperature and the presence of liquid water. A generalization of this concept to computational zones (CZs) is proposed, with constraints set by three principal characteristics: capacity (including computation rates), energy, and instantiation (or substrate, including spatial extent). CZs naturally combine traditional habitability factors, including those associated with biological function that incorporate the chemical milieu, constraints on nutrients and free energy, as well as element availability. Two example applications are presented by examining the fundamental thermodynamic work efficiency and Landauer limit of photon-driven biological computation on planetary surfaces and of generalized computation in stellar energy capture structures (a.k.a. Dyson structures). It is suggested that CZs that involve nested structures or substellar objects could manifest unique observational signatures as cool far-infrared emitters. While these latter scenarios are entirely hypothetical, they offer a useful, complementary introduction to the potential universality of CZs.
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Affiliation(s)
- Caleb Scharf
- NASA Ames Research Center, Moffett Field, California, USA
| | - Olaf Witkowski
- Cross Labs, Cross Compass Ltd., Kyoto, Japan
- College of Arts and Sciences, University of Tokyo, Tokyo, Japan
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26
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Liu S, Akinwande D, Kireev D, Incorvia JAC. Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems. NANO LETTERS 2024. [PMID: 38819288 DOI: 10.1021/acs.nanolett.4c00739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.
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Affiliation(s)
- Samuel Liu
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Deji Akinwande
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Dmitry Kireev
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
- Department of Biomedical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Jean Anne C Incorvia
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas at Austin, Austin, Texas 78758, United States
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27
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Liu L, Wang D, Wang D, Sun Y, Lin H, Gong X, Zhang Y, Tang R, Mai Z, Hou Z, Yang Y, Li P, Wang L, Luo Q, Li L, Xing G, Liu M. Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware. Nat Commun 2024; 15:4534. [PMID: 38806482 PMCID: PMC11133408 DOI: 10.1038/s41467-024-48631-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/06/2024] [Indexed: 05/30/2024] Open
Abstract
We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.
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Affiliation(s)
- Long Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Di Wang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dandan Wang
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China.
| | - Yan Sun
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Huai Lin
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiliang Gong
- Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, China
| | - Yifan Zhang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ruifeng Tang
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Mai
- Hubei Jiufengshan Laboratory, Wuhan, Hubei, 430206, China
| | - Zhipeng Hou
- Institute for Advanced Materials, South China Normal University, Guangzhou, 510006, China
| | - Yumeng Yang
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Peng Li
- School of Microelectronics, University of Science and Technology of China, Hefei, 230026, China
| | - Lan Wang
- Lab of Low Dimensional Magnetism and Spintronic Devices, School of Physics, Hefei University of Technology, Hefei, 230009, Anhui, China
| | - Qing Luo
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling Li
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Guozhong Xing
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Ming Liu
- Key Lab of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
- Frontier Institute of Chip and System, State Key Laboratory of Integrated Chips and Systems, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China.
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28
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Yao M, Richter O, Zhao G, Qiao N, Xing Y, Wang D, Hu T, Fang W, Demirci T, De Marchi M, Deng L, Yan T, Nielsen C, Sheik S, Wu C, Tian Y, Xu B, Li G. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat Commun 2024; 15:4464. [PMID: 38796464 PMCID: PMC11127998 DOI: 10.1038/s41467-024-47811-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 04/12/2024] [Indexed: 05/28/2024] Open
Abstract
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called "Speck", a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the "dynamic imbalance" in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.
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Affiliation(s)
- Man Yao
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ole Richter
- SynSense AG Corporation, Zurich, Switzerland
| | - Guangshe Zhao
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Ning Qiao
- SynSense AG Corporation, Zurich, Switzerland
- SynSense Corporation, Chengdu, Sichuan, China
| | - Yannan Xing
- SynSense Corporation, Chengdu, Sichuan, China
| | - Dingheng Wang
- Northwest Institute of Mechanical & Electrical Engineering, Xianyang, Shaanxi, China
| | - Tianxiang Hu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Fang
- School of Computer Science, Peking University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | | | | | - Lei Deng
- Center for Brain-Inspired Computing, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Carsten Nielsen
- SynSense AG Corporation, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Chenxi Wu
- SynSense AG Corporation, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yonghong Tian
- School of Computer Science, Peking University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Beijing, China.
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29
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Daniel J, Sun Z, Zhang X, Tan Y, Dilley N, Chen Z, Appenzeller J. Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS 2 transistors. Nat Commun 2024; 15:4098. [PMID: 38750065 PMCID: PMC11096331 DOI: 10.1038/s41467-024-48152-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024] Open
Abstract
Probabilistic computing is a computing scheme that offers a more efficient approach than conventional complementary metal-oxide-semiconductor (CMOS)-based logic in a variety of applications ranging from optimization to Bayesian inference, and invertible Boolean logic. The probabilistic bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires tunable stochasticity; by coupling low-barrier stochastic magnetic tunnel junctions (MTJs) with a transistor circuit, a compact implementation is achieved. In this work, by combining stochastic MTJs with 2D-MoS2 field-effect transistors (FETs), we demonstrate an on-chip realization of a p-bit building block displaying voltage-controllable stochasticity. Supported by circuit simulations, we analyze the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component influence the overall p-bit output. While the current approach has not reached the level of maturity required to compete with CMOS-compatible MTJ technology, the design rules presented in this work are valuable for future experimental implementations of scaled on-chip p-bit networks with reduced footprint.
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Affiliation(s)
- John Daniel
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA.
- Department of Physics and Astronomy, Purdue University, West Lafayette, IN, 47907, USA.
| | - Zheng Sun
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Xuejian Zhang
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Yuanqiu Tan
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Neil Dilley
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA
| | - Zhihong Chen
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Joerg Appenzeller
- Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA.
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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30
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Bag A, Ghosh G, Sultan MJ, Chouhdry HH, Hong SJ, Trung TQ, Kang GY, Lee NE. Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024:e2403150. [PMID: 38699932 DOI: 10.1002/adma.202403150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/16/2024] [Indexed: 05/05/2024]
Abstract
In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.
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Affiliation(s)
- Atanu Bag
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Gargi Ghosh
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - M Junaid Sultan
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Hamna Haq Chouhdry
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Seok Ju Hong
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Tran Quang Trung
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Geun-Young Kang
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
| | - Nae-Eung Lee
- School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Research Centre for Advanced Materials Technology, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Institute of Quantum Biophysics (IQB) and Biomedical Institute for Convergence at SKKU (BICS), Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
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31
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Alonso N, Krichmar JL. A sparse quantized hopfield network for online-continual memory. Nat Commun 2024; 15:3722. [PMID: 38697981 PMCID: PMC11065890 DOI: 10.1038/s41467-024-46976-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024] Open
Abstract
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local learning algorithms and are trained in an offline, non-noisy, independent, identically distributed setting. Understanding how neural networks learn under the same constraints as the brain is an open problem for neuroscience and neuromorphic computing. A standard approach to this problem has yet to be established. In this paper, we propose that discrete graphical models that learn via an online maximum a posteriori learning algorithm could provide such an approach. We implement this kind of model in a neural network called the Sparse Quantized Hopfield Network. We show our model outperforms state-of-the-art neural networks on associative memory tasks, outperforms these networks in online, continual settings, learns efficiently with noisy inputs, and is better than baselines on an episodic memory task.
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Affiliation(s)
- Nicholas Alonso
- Department of Cognitive Science, University of California, Irvine, CA, USA.
| | - Jeffrey L Krichmar
- Department of Cognitive Science, University of California, Irvine, CA, USA
- Department Computer Science, University of California, Irvine, CA, USA
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32
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Xie J, Zhou Y, Faizan M, Li Z, Li T, Fu Y, Wang X, Zhang L. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies. NATURE COMPUTATIONAL SCIENCE 2024; 4:322-333. [PMID: 38783137 DOI: 10.1038/s43588-024-00632-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
In the post-Moore's law era, the progress of electronics relies on discovering superior semiconductor materials and optimizing device fabrication. Computational methods, augmented by emerging data-driven strategies, offer a promising alternative to the traditional trial-and-error approach. In this Perspective, we highlight data-driven computational frameworks for enhancing semiconductor discovery and device development by elaborating on their advances in exploring the materials design space, predicting semiconductor properties and optimizing device fabrication, with a concluding discussion on the challenges and opportunities in these areas.
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Affiliation(s)
- Jiahao Xie
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yansong Zhou
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Muhammad Faizan
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Zewei Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Tianshu Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China
| | - Yuhao Fu
- State Key Laboratory of Superhard Materials, International Center of Computational Method and Software, School of Physics, Jilin University, Changchun, China
| | - Xinjiang Wang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
| | - Lijun Zhang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, Key Laboratory of Material Simulation Methods & Software of MOE, and School of Materials Science and Engineering, Jilin University, Changchun, China.
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33
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Nibhanupudi SST, Roy A, Chowdhury S, Schalip R, Coupin MJ, Matthews KC, Alam MH, Satpati B, Movva HCP, Luth CJ, Wu S, Warner JH, Banerjee SK. Low-Temperature Synthesis of WSe 2 by the Selenization Process under Ultrahigh Vacuum for BEOL Compatible Reconfigurable Neurons. ACS APPLIED MATERIALS & INTERFACES 2024; 16:22326-22333. [PMID: 38635965 DOI: 10.1021/acsami.3c18446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Low-temperature large-area growth of two-dimensional (2D) transition-metal dichalcogenides (TMDs) is critical for their integration with silicon chips. Especially, if the growth temperatures can be lowered below the back-end-of-line (BEOL) processing temperatures, the Si transistors can interface with 2D devices (in the back end) to enable high-density heterogeneous circuits. Such configurations are particularly useful for neuromorphic computing applications where a dense network of neurons interacts to compute the output. In this work, we present low-temperature synthesis (400 °C) of 2D tungsten diselenide (WSe2) via the selenization of the W film under ultrahigh vacuum (UHV) conditions. This simple yet effective process yields large-area, homogeneous films of 2D TMDs, as confirmed by several characterization techniques, including reflection high-energy electron diffraction, atomic force microscopy, transmission electron microscopy, and different spectroscopy methods. Memristors fabricated using the grown WSe2 film are leveraged to realize a novel compact neuron circuit that can be reconfigured to enable homeostasis.
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Affiliation(s)
- S S Teja Nibhanupudi
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Anupam Roy
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
- Department of Physics, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835215, India
| | - Sayema Chowdhury
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Ryan Schalip
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Matthew J Coupin
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Kevin C Matthews
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Md Hasibul Alam
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Biswarup Satpati
- Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, 1/AF, Bidhannagar, Kolkata 700 064, India
| | - Hema C P Movva
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Christopher J Luth
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Siyu Wu
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
| | - Jamie H Warner
- Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Sanjay K Banerjee
- Microelectronics Research Center, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78758, United States
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34
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Laydevant J, Marković D, Grollier J. Training an Ising machine with equilibrium propagation. Nat Commun 2024; 15:3671. [PMID: 38693108 PMCID: PMC11063034 DOI: 10.1038/s41467-024-46879-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 03/12/2024] [Indexed: 05/03/2024] Open
Abstract
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine's connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications.
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Affiliation(s)
- Jérémie Laydevant
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
| | - Danijela Marković
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Julie Grollier
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
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35
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Kamsma TM, Kim J, Kim K, Boon WQ, Spitoni C, Park J, van Roij R. Brain-inspired computing with fluidic iontronic nanochannels. Proc Natl Acad Sci U S A 2024; 121:e2320242121. [PMID: 38657046 PMCID: PMC11067030 DOI: 10.1073/pnas.2320242121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/19/2024] [Indexed: 04/26/2024] Open
Abstract
The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.
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Affiliation(s)
- Tim M. Kamsma
- Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht3584, The Netherlands
- Mathematical Institute, Department of Mathematics, Utrecht University, Utrecht3584, The Netherlands
| | - Jaehyun Kim
- Department of Mechanical Engineering, Sogang University, Seoul04107, Republic of Korea
| | - Kyungjun Kim
- Department of Mechanical Engineering, Sogang University, Seoul04107, Republic of Korea
| | - Willem Q. Boon
- Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht3584, The Netherlands
| | - Cristian Spitoni
- Mathematical Institute, Department of Mathematics, Utrecht University, Utrecht3584, The Netherlands
| | - Jungyul Park
- Department of Mechanical Engineering, Sogang University, Seoul04107, Republic of Korea
| | - René van Roij
- Institute for Theoretical Physics, Department of Physics, Utrecht University, Utrecht3584, The Netherlands
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36
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Koren V, Malerba SB, Schwalger T, Panzeri S. Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590955. [PMID: 38712237 PMCID: PMC11071478 DOI: 10.1101/2024.04.24.590955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuro-science, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we rigorously derive the structural, coding, biophysical and dynamical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-stimulus-specific excitatory external input regulating metabolic cost. The efficient network has excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implementing feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal biophysical parameters include 4 to 1 ratio of excitatory vs inhibitory neurons and 3 to 1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity that closely match those of cortical sensory networks. The efficient network has biologically-plausible spiking dynamics, with a tight instantaneous E-I balance that makes them capable to achieve efficient coding of external stimuli varying over multiple time scales. Together, these results explain how efficient coding may be implemented in cortical networks and suggests that key properties of biological neural networks may be accounted for by efficient coding.
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Park J, Kumar A, Zhou Y, Oh S, Kim JH, Shi Y, Jain S, Hota G, Qiu E, Nagle AL, Schuller IK, Schuman CD, Cauwenberghs G, Kuzum D. Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge. Nat Commun 2024; 15:3492. [PMID: 38664381 PMCID: PMC11045755 DOI: 10.1038/s41467-024-46682-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/06/2024] [Indexed: 04/28/2024] Open
Abstract
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.
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Affiliation(s)
- Jaeseoung Park
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Ashwani Kumar
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Yucheng Zhou
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Sangheon Oh
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Jeong-Hoon Kim
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Yuhan Shi
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Soumil Jain
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Gopabandhu Hota
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Erbin Qiu
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Amelie L Nagle
- Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ivan K Schuller
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | - Catherine D Schuman
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, USA
| | - Gert Cauwenberghs
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
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38
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Galloni AR, Yuan Y, Zhu M, Yu H, Bisht RS, Wu CTM, Grienberger C, Ramanathan S, Milstein AD. Neuromorphic one-shot learning utilizing a phase-transition material. Proc Natl Acad Sci U S A 2024; 121:e2318362121. [PMID: 38630718 PMCID: PMC11047090 DOI: 10.1073/pnas.2318362121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/25/2024] [Indexed: 04/19/2024] Open
Abstract
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.
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Affiliation(s)
- Alessandro R. Galloni
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ08854
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Yifan Yuan
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Minning Zhu
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Haoming Yu
- School of Materials Engineering, Purdue University, West Lafayette, IN47907
| | - Ravindra S. Bisht
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Chung-Tse Michael Wu
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Christine Grienberger
- Department of Neuroscience, Brandeis University, Waltham, MA02453
- Department of Biology and Volen National Center for Complex Systems, Brandeis University, Waltham, MA02453
| | - Shriram Ramanathan
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Aaron D. Milstein
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ08854
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ08854
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Khonina SN, Kazanskiy NL, Skidanov RV, Butt MA. Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:697. [PMID: 38668191 PMCID: PMC11054149 DOI: 10.3390/nano14080697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024]
Abstract
Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light's properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.
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Affiliation(s)
| | | | | | - Muhammad A. Butt
- Samara National Research University, 443086 Samara, Russia (N.L.K.)
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40
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Donati G, Argyris A, Mancinelli M, Mirasso CR, Pavesi L. Time delay reservoir computing with a silicon microring resonator and a fiber-based optical feedback loop. OPTICS EXPRESS 2024; 32:13419-13437. [PMID: 38859313 DOI: 10.1364/oe.514617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/19/2024] [Indexed: 06/12/2024]
Abstract
Silicon microring resonators serve as critical components in integrated photonic neural network implementations, owing to their compact footprint, compatibility with CMOS technology, and passive nonlinear dynamics. Recent advancements have leveraged their filtering properties as weighting functions, and their nonlinear dynamics as activation functions with spiking capabilities. In this work, we investigate experimentally the linear and nonlinear dynamics of microring resonators for time delay reservoir computing, by introducing an external optical feedback loop. After effectively mitigating the impact of environmental noise on the fiber-based feedback phase dependencies, we evaluate the computational capacity of this system by assessing its performance across various benchmark tasks at a bit rate of few Mbps. We show that the additional memory provided by the optical feedback is necessary to achieve error-free operation in delayed-boolean tasks that require up to 3 bits of memory. In this case the microring was operated in the linear regime and the photodetection was the nonlinear activation function. We also show that the Santa Fe and Mackey Glass prediction tasks are solved when the microring nonlinearities are activated. Notably, our study reveals competitive outcomes even when employing only 7 virtual nodes within our photonic reservoir. Our findings illustrate the silicon microring's versatile performance in the presence of optical feedback, highlighting its ability to be tailored for various computing applications.
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41
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Pagkalos M, Makarov R, Poirazi P. Leveraging dendritic properties to advance machine learning and neuro-inspired computing. Curr Opin Neurobiol 2024; 85:102853. [PMID: 38394956 DOI: 10.1016/j.conb.2024.102853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information, using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training while still struggling to compete in tasks that are trivial for biological agents. Thus, brain-inspired engineering has emerged as a promising new avenue for designing sustainable, next-generation AI systems. Here, we describe how dendritic mechanisms of biological neurons have inspired innovative solutions for significant AI problems, including credit assignment in multi-layer networks, catastrophic forgetting, and high-power consumption. These findings provide exciting alternatives to existing architectures, showing how dendritic research can pave the way for building more powerful and energy efficient artificial learning systems.
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Affiliation(s)
- Michalis Pagkalos
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/MPagkalos
| | - Roman Makarov
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece; Department of Biology, University of Crete, Heraklion, 70013, Greece. https://twitter.com/_RomanMakarov
| | - Panayiota Poirazi
- Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH), Heraklion, 70013, Greece.
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42
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Milozzi A, Ricci S, Ielmini D. Memristive tonotopic mapping with volatile resistive switching memory devices. Nat Commun 2024; 15:2812. [PMID: 38561389 PMCID: PMC10985068 DOI: 10.1038/s41467-024-47228-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems.
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Affiliation(s)
- Alessandro Milozzi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
| | - Saverio Ricci
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
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43
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Zuo Y, Ning N, Qiao GC, Wu JH, Bao JH, Zhang XY, Bai J, Wu FH, Liu Y, Yu Q, Hu SG. Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024; 18:347-360. [PMID: 37878421 DOI: 10.1109/tbcas.2023.3327496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.
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44
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Xu Y, Shidqi K, van Schaik GJ, Bilgic R, Dobrita A, Wang S, Meijer R, Nembhani P, Arjmand C, Martinello P, Gebregiorgis A, Hamdioui S, Detterer P, Traferro S, Konijnenburg M, Vadivel K, Sifalakis M, Tang G, Yousefzadeh A. Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration. Front Neurosci 2024; 18:1335422. [PMID: 38606307 PMCID: PMC11007209 DOI: 10.3389/fnins.2024.1335422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/28/2024] [Indexed: 04/13/2024] Open
Abstract
Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory computing are the two dominant design trends of neuromorphic processors. However, there remain challenges in reducing the overhead of event-driven processing and increasing the mapping efficiency of near/in-memory computing, which directly impacts the performance and area efficiency. In this work, we discuss these challenges and present our exploration of optimizing event-based neural network inference on SENECA, a scalable and flexible neuromorphic architecture. To address the overhead of event-driven processing, we perform comprehensive design space exploration and propose spike-grouping to reduce the total energy and latency. Furthermore, we introduce the event-driven depth-first convolution to increase area efficiency and latency in convolutional neural networks (CNNs) on the neuromorphic processor. We benchmarked our optimized solution on keyword spotting, sensor fusion, digit recognition and high resolution object detection tasks. Compared with other state-of-the-art large-scale neuromorphic processors, our proposed optimizations result in a 6× to 300× improvement in energy efficiency, a 3× to 15× improvement in latency, and a 3× to 100× improvement in area efficiency. Our optimizations for event-based neural networks can be potentially generalized to a wide range of event-based neuromorphic processors.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Anteneh Gebregiorgis
- Department of Dependable and Emerging Computer Technologies, Delft University of Technology, Delft, Netherlands
| | - Said Hamdioui
- Department of Dependable and Emerging Computer Technologies, Delft University of Technology, Delft, Netherlands
| | | | | | | | | | | | | | - Amirreza Yousefzadeh
- IMEC, Eindhoven, Netherlands
- Department of Computer Architecture and Embedded Systems, University of Twente, Enschede, Netherlands
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45
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Farzin MA, Naghib SM, Rabiee N. Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications. ACS Biomater Sci Eng 2024; 10:1262-1301. [PMID: 38376103 DOI: 10.1021/acsbiomaterials.3c01633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
The rapid maturation of smart city ecosystems is intimately linked to advances in the Internet of Things (IoT) and self-powered sensing technologies. Central to this evolution are battery-less sensors that are critical for applications such as continuous health monitoring through blood metabolites and vital signs, the recognition of human activity for behavioral analysis, and the operational enhancement of humanoid robots. The focus on biosensors that exploit the human body for energy-spanning wearable, attachable, and implantable variants has intensified, driven by their broad applicability in areas from underwater exploration to biomedical assays and earthquake monitoring. The heart of these sensors lies in their diverse energy harvesting mechanisms, including biofuel cells, and piezoelectric, triboelectric, and pyroelectric nanogenerators. Notwithstanding the wealth of research, the literature still lacks a holistic review that integrates the design challenges and implementation intricacies of such sensors. Our review seeks to fill this gap by thoroughly evaluating energy harvesting strategies from both material and structural perspectives and assessing their roles in powering an array of sensors for myriad uses. This exploration offers a comprehensive outlook on the state of self-powered sensing devices, tackling the nuances of their deployment and highlighting their potential to revolutionize data gathering in autonomous systems. The intent of this review is to chart the current landscape and future prospects, providing a pivotal reference point for ongoing research and innovation in self-powered wireless sensing technologies.
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Affiliation(s)
- Mohammad Ali Farzin
- Nanotechnology Department, School of Advanced Technologies, Iran University of Science and Technology, P.O. Box 16846-13114, Tehran 13114-16846, Iran
| | - Seyed Morteza Naghib
- Nanotechnology Department, School of Advanced Technologies, Iran University of Science and Technology, P.O. Box 16846-13114, Tehran 13114-16846, Iran
| | - Navid Rabiee
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA 6150, Australia
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46
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Yan M, Huang C, Bienstman P, Tino P, Lin W, Sun J. Emerging opportunities and challenges for the future of reservoir computing. Nat Commun 2024; 15:2056. [PMID: 38448438 PMCID: PMC10917819 DOI: 10.1038/s41467-024-45187-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 01/16/2024] [Indexed: 03/08/2024] Open
Abstract
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.
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Affiliation(s)
- Min Yan
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China
| | - Can Huang
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China.
| | - Peter Bienstman
- Photonics Research Group, Department of Information Technology, Ghent University, Gent, Belgium
| | - Peter Tino
- School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Wei Lin
- Research Institute of Intelligent Complex Systems, Fudan University, Shanghai, 200433, China
- School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University, Shanghai, 200433, China
| | - Jie Sun
- Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd., Hong Kong SAR, China.
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47
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Xi J, Yang H, Li X, Wei R, Zhang T, Dong L, Yang Z, Yuan Z, Sun J, Hua Q. Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:465. [PMID: 38470794 DOI: 10.3390/nano14050465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024]
Abstract
Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.
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Affiliation(s)
- Jianguo Xi
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Huaiwen Yang
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Xinyu Li
- School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Ruilai Wei
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
| | - Taiping Zhang
- Tianfu Xinglong Lake Laboratory, Chengdu 610299, China
| | - Lin Dong
- Henan Key Laboratory of Diamond Optoelectronic Materials and Devices, Key Laboratory of Materials Physics, Ministry of Education, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
| | - Zhenjun Yang
- Hefei Hospital Affiliated to Anhui Medical University (The Second People's Hospital of Hefei), Hefei 230011, China
| | - Zuqing Yuan
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
| | - Junlu Sun
- Henan Key Laboratory of Diamond Optoelectronic Materials and Devices, Key Laboratory of Materials Physics, Ministry of Education, School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
| | - Qilin Hua
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
- Institute of Flexible Electronics, Beijing Institute of Technology, Beijing 102488, China
- Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, Guangxi Normal University, Guilin 541004, China
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48
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Li S, Guo Z, Zang X. Advancing the Production of Clinical Medical Devices Through ChatGPT. Ann Biomed Eng 2024; 52:441-445. [PMID: 37369944 DOI: 10.1007/s10439-023-03300-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 06/29/2023]
Abstract
As a recently popular large language model, Chatbot Generative Pre-trained Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to the limited understanding of the potential impact of ChatGPT on the manufacturing side of clinical medical devices, we aim to fill this gap through this article. We elucidate the classification of medical devices and explore the positive contributions of ChatGPT in various aspects of medical device design, optimization, and improvement. However, limitations such as the potential for misinterpretation of user intent, lack of personal experience, and the need for human supervision should be taken into consideration. Striking a balance between ChatGPT and human expertise can ensure the safety, quality, and compliance of medical devices. This work contributes to the advancement of ChatGPT in the medical device manufacturing industry and highlights the synergistic relationship between artificial intelligence and human involvement in healthcare.
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Affiliation(s)
- Siqi Li
- Advanced Research Center, GD Midea Equipment Co., Ltd, Foshan, 528000, China
| | - Zheng Guo
- Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, 528042, China.
| | - Xuehui Zang
- Orthopedics Department of The Sixth Affiliated Hospital, School of Medicine, South China University of Technology, Foshan, 528042, China.
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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50
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Wu G, Xiang L, Wang W, Yao C, Yan Z, Zhang C, Wu J, Liu Y, Zheng B, Liu H, Hu C, Sun X, Zhu C, Wang Y, Xiong X, Wu Y, Gao L, Li D, Pan A, Li S. Hierarchical processing enabled by 2D ferroelectric semiconductor transistor for low-power and high-efficiency AI vision system. Sci Bull (Beijing) 2024; 69:473-482. [PMID: 38123429 DOI: 10.1016/j.scib.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/23/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
The growth of data and Internet of Things challenges traditional hardware, which encounters efficiency and power issues owing to separate functional units for sensors, memory, and computation. In this study, we designed an α-phase indium selenide (α-In2Se3) transistor, which is a two-dimensional ferroelectric semiconductor as the channel material, to create artificial optic-neural and electro-neural synapses, enabling cutting-edge processing-in-sensor (PIS) and computing-in-memory (CIM) functionalities. As an optic-neural synapse for low-level sensory processing, the α-In2Se3 transistor exhibits a high photoresponsivity (2855 A/W) and detectivity (2.91 × 1014 Jones), facilitating efficient feature extraction. For high-level processing tasks as an electro-neural synapse, it offers a fast program/erase speed of 40 ns/50 µs and ultralow energy consumption of 0.37 aJ/spike. An AI vision system using α-In2Se3 transistors has been demonstrated. It achieved an impressive recognition accuracy of 92.63% within 12 epochs owing to the synergistic combination of the PIS and CIM functionalities. This study demonstrates the potential of the α-In2Se3 transistor in future vision hardware, enhancing processing, power efficiency, and AI applications.
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Affiliation(s)
- Guangcheng Wu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Li Xiang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Wenqiang Wang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Chengdong Yao
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Zeyi Yan
- Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan 411105, China
| | - Cheng Zhang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Jiaxin Wu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Yong Liu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Biyuan Zheng
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Huawei Liu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Chengwei Hu
- Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan 411105, China
| | - Xingxia Sun
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Chenguang Zhu
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Yizhe Wang
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China
| | - Xiong Xiong
- School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Yanqing Wu
- Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan 411105, China; School of Integrated Circuits, Peking University, Beijing 100871, China
| | - Liang Gao
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan 430074, China
| | - Dong Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China.
| | - Anlian Pan
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China.
| | - Shengman Li
- Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Materials Science and Engineering, Hunan University, Changsha 410082, China; Hunan Institute of Optoelectronic Integration, Hunan University, Changsha 410082, China.
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