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Xu K, Wang T, Lu C, Song Y, Liu Y, Yu J, Liu Y, Li Z, Meng J, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing. NANO LETTERS 2024. [PMID: 39148056 DOI: 10.1021/acs.nanolett.4c02142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.
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Affiliation(s)
- Kangli Xu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Tianyu Wang
- School of Integrated Circuits, Shandong University, Jinan 250100, China
- Suzhou Research Institute of Shandong University, Suzhou 215123, China
- State Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai 200050, China
| | - Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yifan Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yongkai Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yinchi Liu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Zhenhai Li
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
- Jiashan Fudan Institute, Jiaxing 314110, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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2
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Hore A, Bandyopadhyay S, Chakrabarti S. Persistent spiking activity in neuromorphic circuits incorporating post-inhibitory rebound excitation. J Neural Eng 2024; 21:036048. [PMID: 38861961 DOI: 10.1088/1741-2552/ad56c8] [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: 01/09/2024] [Accepted: 06/11/2024] [Indexed: 06/13/2024]
Abstract
Objective. This study introduces a novel approach for integrating the post-inhibitory rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory synapses are designed to establish a connection between two hardware neurons, effectively forming a network. The model demonstrates the occurrence of PIRE under strong inhibitory input. Emphasizing the significance of incorporating PIRE in neuromorphic circuits, the study showcases generation of persistent activity within cyclic and recurrent spiking neuronal networks.Approach. The neuronal and synaptic circuits are designed and simulated in Cadence Virtuoso using TSMC 180 nm technology. The operating mechanism of the PIRE phenomenon integrated into a hardware neuron is discussed. The proposed circuit encompasses several parameters for effectively controlling multiple electrophysiological features of a neuron.Main results. The neuronal circuit has been tuned to match the response of a biological neuron. The efficiency of this circuit is evaluated by computing the average power dissipation and energy consumption per spike through simulation. The sustained firing of neural spikes is observed till 1.7 s using the two neuronal networks.Significance. Persistent activity has significant implications for various cognitive functions such as working memory, decision-making, and attention. Therefore, hardware implementation of these functions will require our PIRE-integrated model. Energy-efficient neuromorphic systems are useful in many artificial intelligence applications, including human-machine interaction, IoT devices, autonomous systems, and brain-computer interfaces.
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Ahsan R, Wu Z, Jalal SA, Kapadia R. Ultralow Power Electronic Analog of a Biological Fitzhugh-Nagumo Neuron. ACS OMEGA 2024; 9:18062-18071. [PMID: 38680341 PMCID: PMC11044232 DOI: 10.1021/acsomega.3c09936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/14/2024] [Accepted: 03/26/2024] [Indexed: 05/01/2024]
Abstract
Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh-Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NP-complete max-cut problems while showing robustness toward process variations.
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Affiliation(s)
- Ragib Ahsan
- Department of Electrical
and Computer Engineering, University of
Southern California, Los Angeles 90089-0001, United
States
| | - Zezhi Wu
- Department of Electrical
and Computer Engineering, University of
Southern California, Los Angeles 90089-0001, United
States
| | - Seyedeh Atiyeh
Abbasi Jalal
- Department of Electrical
and Computer Engineering, University of
Southern California, Los Angeles 90089-0001, United
States
| | - Rehan Kapadia
- Department of Electrical
and Computer Engineering, University of
Southern California, Los Angeles 90089-0001, United
States
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4
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Rao A, Sanjay S, Dey V, Ahmadi M, Yadav P, Venugopalrao A, Bhat N, Kooi B, Raghavan S, Nukala P. Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform. MATERIALS HORIZONS 2023; 10:5235-5245. [PMID: 37740285 DOI: 10.1039/d3mh01000g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate a system comprising of multilayer hexagonal boron nitride (hBN) films contacted with silver (Ag), which can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between the high resistance state (HRS) and the low resistance state (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, two different neural networks are realized in a single CMOS compatible, 2D material platform.
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Affiliation(s)
- Ankit Rao
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Sooraj Sanjay
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Vivek Dey
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Majid Ahmadi
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
| | - Pramod Yadav
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Anirudh Venugopalrao
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Navakanta Bhat
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Bart Kooi
- Zernike Institute for Advanced Materials, University of Groningen, Groningen, The Netherlands
- CogniGron Center, University of Groningen, Groningen, 9747 AG, The Netherlands
| | - Srinivasan Raghavan
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
| | - Pavan Nukala
- Centre for Nano Science and Engineering, Indian Institute of Science, Bengaluru 560012, India.
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5
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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6
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Chien YC, Xiang H, Shi Y, Duong NT, Li S, Ang KW. A MoS 2 Hafnium Oxide Based Ferroelectric Encoder for Temporal-Efficient Spiking Neural Network. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204949. [PMID: 36366910 DOI: 10.1002/adma.202204949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/26/2022] [Indexed: 06/16/2023]
Abstract
Spiking neural network (SNN), where the information is evaluated recurrently through spikes, has manifested significant promises to minimize the energy expenditure in data-intensive machine learning and artificial intelligence. Among these applications, the artificial neural encoders are essential to convert the external stimuli to a spiking format that can be subsequently fed to the neural network. Here, a molybdenum disulfide (MoS2 ) hafnium oxide-based ferroelectric encoder is demonstrated for temporal-efficient information processing in SNN. The fast domain switching attribute associated with the polycrystalline nature of hafnium oxide-based ferroelectric material is exploited for spike encoding, rendering it suitable for realizing biomimetic encoders. Accordingly, a high-performance ferroelectric encoder is achieved, featuring a superior switching efficiency, negligible charge trapping effect, and robust ferroelectric response, which successfully enable a broad dynamic range. Furthermore, an SNN is simulated to verify the precision of the encoded information, in which an average inference accuracy of 95.14% can be achieved, using the Modified National Insitute of Standards and Technology (MNIST) dataset for digit classification. Moreover, this ferroelectric encoder manifests prominent resilience against noise injection with an overall prediction accuracy of 94.73% under various Gaussian noise levels, showing practical promises to reduce the computational load for the neural network.
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Affiliation(s)
- Yu-Chieh Chien
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Heng Xiang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Yufei Shi
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Ngoc Thanh Duong
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore
- Institute of Materials Research and Engineering, A*STAR, 2 Fusionopolis Way, Singapore, 138634, Singapore
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7
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Subbulakshmi Radhakrishnan S, Chakrabarti S, Sen D, Das M, Schranghamer TF, Sebastian A, Das S. A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2202535. [PMID: 35674268 DOI: 10.1002/adma.202202535] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/31/2022] [Indexed: 06/15/2023]
Abstract
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that information in the brain is more often represented by explicit firing times of the neurons rather than mean firing rates, it is imperative to develop novel hardware that can accelerate sparse and spike-timing-based encoding. Here a medium-scale integrated circuit composed of two cascaded three-stage inverters and one XOR logic gate fabricated using a total of 21 memtransistors based on photosensitive 2D monolayer MoS2 for spike-timing-based encoding of visual information, is introduced. It is shown that different illumination intensities can be encoded into sparse spiking with time-to-first-spike representing the illumination information, that is, higher intensities invoke earlier spikes and vice versa. In addition, non-volatile and analog programmability in the photoencoder is exploited for adaptive photoencoding that allows expedited spiking under scotopic (low-light) and deferred spiking under photopic (bright-light) conditions, respectively. Finally, low energy expenditure of less than 1 µJ by the 2D-memtransistor-based photoencoder highlights the benefits of in-sensor and bioinspired design that can be transformative for the acceleration of SNNs.
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Affiliation(s)
| | - Shakya Chakrabarti
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
| | - Dipanjan Sen
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Mayukh Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Thomas F Schranghamer
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Amritanand Sebastian
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
| | - Saptarshi Das
- Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA
- Electrical Engineering and Computer Science, Penn State University, University Park, PA, 16802, USA
- Materials Science and Engineering, Penn State University, University Park, PA, 16802, USA
- Materials Research Institute, Penn State University, University Park, PA, 16802, USA
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8
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Compact artificial neuron based on anti-ferroelectric transistor. Nat Commun 2022; 13:7018. [PMID: 36384960 PMCID: PMC9668812 DOI: 10.1038/s41467-022-34774-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/07/2022] [Indexed: 11/18/2022] Open
Abstract
Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf0.2Zr0.8O2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf0.2Zr0.8O2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>1012), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware.
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9
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Udaya Mohanan K, Cho S, Park BG. Optimization of the structural complexity of artificial neural network for hardware-driven neuromorphic computing application. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03783-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractThis work focuses on the optimization of the structural complexity of a single-layer feedforward neural network (SLFN) for neuromorphic hardware implementation. The singular value decomposition (SVD) method is used for the determination of the effective number of neurons in the hidden layer for Modified National Institute of Standards and Technology (MNIST) dataset classification. The proposed method is also verified on a SLFN using weights derived from a synaptic transistor device. The effectiveness of this methodology in estimating the reduced number of neurons in the hidden layer makes this method highly useful in optimizing complex neural network architectures for their hardware realization.
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10
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Liu F, Deswal S, Christou A, Sandamirskaya Y, Kaboli M, Dahiya R. Neuro-inspired electronic skin for robots. Sci Robot 2022; 7:eabl7344. [PMID: 35675450 DOI: 10.1126/scirobotics.abl7344] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.
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Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | | | - Mohsen Kaboli
- Department of Research, New Technologies, Innovation, BMW Group, Parkring 19, 85748 Garching bei Munchen, Germany.,Cognitive Robotics and Tactile Intelligence Group, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
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11
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Liu F, Deswal S, Christou A, Shojaei Baghini M, Chirila R, Shakthivel D, Chakraborty M, Dahiya R. Printed synaptic transistor-based electronic skin for robots to feel and learn. Sci Robot 2022; 7:eabl7286. [PMID: 35648845 DOI: 10.1126/scirobotics.abl7286] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.
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Affiliation(s)
- Fengyuan Liu
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Sweety Deswal
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Adamos Christou
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Mahdieh Shojaei Baghini
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Radu Chirila
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Dhayalan Shakthivel
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Moupali Chakraborty
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
| | - Ravinder Dahiya
- Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK
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12
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Superconducting Bio-Inspired Au-Nanowire-Based Neurons. NANOMATERIALS 2022; 12:nano12101671. [PMID: 35630895 PMCID: PMC9147065 DOI: 10.3390/nano12101671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 02/01/2023]
Abstract
High-performance modeling of neurophysiological processes is an urgent task that requires new approaches to information processing. In this context, two- and three-junction superconducting quantum interferometers with Josephson weak links based on gold nanowires are fabricated and investigated experimentally. The studied cells are proposed for the implementation of bio-inspired neurons—high-performance, energy-efficient, and compact elements of neuromorphic processor. The operation modes of an advanced artificial neuron capable of generating the burst firing activation patterns are explored theoretically. A comparison with the Izhikevich mathematical model of biological neurons is carried out.
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13
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An Ultra-Low Power Threshold Voltage Variable Artificial Retina Neuron. ELECTRONICS 2022. [DOI: 10.3390/electronics11030365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed frequencies under different light intensities. The power consumption is reduced by removing the film capacitor. The comparator is adopted to improve the stability of the circuit, and the power consumption of the comparator is optimized. The power consumption of the proposed CMOS neuron circuit is suppressed. The ultra-low-power artificial neuron with variable threshold shows a frequency range of 0.8–80 kHz when the input current is varied from 1 pA to 150 pA. The minimum DC power is 35 pW when the input current is 5 pA. The minimum energy of the neuron is 3 fJ. The proposed ultra-low-power artificial retina neuron has wide potential applications in the field of AI.
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14
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Yajima T. Ultra-low-power switching circuits based on a binary pattern generator with spiking neurons. Sci Rep 2022; 12:1150. [PMID: 35064156 PMCID: PMC8782828 DOI: 10.1038/s41598-022-04982-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/05/2022] [Indexed: 11/09/2022] Open
Abstract
Research on various neuro-inspired technologies has received much attention. However, while higher-order neural functions such as recognition have been emphasized, the fundamental properties of neural circuits as advanced control systems have not been fully exploited. Here, we applied the functions of central pattern generators, biological neural circuits for motor control, to the control technology of switching circuits for extremely power-saving terminal edge devices. By simply applying a binary waveform with an arbitrary temporal pattern to the transistor gate, low-power and real-time switching control can be achieved. This binary pattern generator consists of a specially designed spiking neuron circuit that generates spikes after a pre-programmed wait time in the six-order range, but consumes negligible power, with an experimental record of 1.2 pW per neuron. This control scheme has been successfully applied to voltage conversion circuits consuming only a few nanowatts, providing an ultra-low power technology for trillions of self-powered edge systems.
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Affiliation(s)
- Takeaki Yajima
- Department of Electronics, Kyushu University, Fukuoka-shi, Fukuoka, 819-0395, Japan.
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15
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Buckwell M, Ng WH, Mannion DJ, Cox HRJ, Hudziak S, Mehonic A, Kenyon AJ. Neuromorphic Dynamics at the Nanoscale in Silicon Suboxide RRAM. FRONTIERS IN NANOTECHNOLOGY 2021. [DOI: 10.3389/fnano.2021.699037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic applications due to their scalability, low-power operation and diverse functional behaviors. However, understanding the dynamics of individual filaments, and the surrounding material, is challenging, owing to the typically very large cross-sectional areas of test devices relative to the nanometer scale of individual filaments. In the present work, conductive atomic force microscopy is used to study the evolution of conductivity at the nanoscale in a fully CMOS-compatible silicon suboxide thin film. Distinct filamentary plasticity and background conductivity enhancement are reported, suggesting that device behavior might be best described by composite core (filament) and shell (background conductivity) dynamics. Furthermore, constant current measurements demonstrate an interplay between filament formation and rupture, resulting in current-controlled voltage spiking in nanoscale regions, with an estimated optimal energy consumption of 25 attojoules per spike. This is very promising for extremely low-power neuromorphic computation and suggests that the dynamic behavior observed in larger devices should persist and improve as dimensions are scaled down.
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16
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Han JK, Oh J, Yu JM, Choi SY, Choi YK. A Vertical Silicon Nanowire Based Single Transistor Neuron with Excitatory, Inhibitory, and Myelination Functions for Highly Scalable Neuromorphic Hardware. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2103775. [PMID: 34605173 DOI: 10.1002/smll.202103775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Indexed: 06/13/2023]
Abstract
A single transistor neuron (1T-neuron) is demonstrated by using a vertically protruded nanowire from an 8 in. silicon (Si) wafer. The 1T-neuron adopts a gate-all-around structure to completely surround the Si nanowire (Si-NW) to make a floating body and allow aggressive downscaling. The Si-NW is composed of an n+ drain at the top, n+ source at the bottom, and p-type floating body at the middle, which are self-aligned vertically. Thus, it occupies a small footprint area. The gate controls an excitatory/inhibitory function. In addition, myelination of a biological neuron that changes membrane capacitance is mimicked by an inherently asymmetric source/drain structure. Two spiking frequencies at the same input current are controlled by whether the neuron is myelinated or unmyelinated. Using the vertical 1T-neuron, pattern recognition is demonstrated with both measurements and semiempirical circuit simulations. Furthermore, handwritten numbers in the MNIST database are recognized with accuracy of 93% by software-based simulations. Applicability of the vertical 1T-neuron to various neural networks is verified, including a single-layer perceptron, multilayer perceptron, and spiking neural network.
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Affiliation(s)
- Joon-Kyu Han
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Jungyeop Oh
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Ji-Man Yu
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Sung-Yool Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
| | - Yang-Kyu Choi
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon, Yuseong-gu, 34141, Republic of Korea
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17
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Zare M, Zafarkhah E, S. Anzabi-Nezhad N. An area and energy efficient LIF neuron model with spike frequency adaptation mechanism. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Primavera BA, Shainline JM. Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware. Front Neurosci 2021; 15:732368. [PMID: 34552465 PMCID: PMC8450355 DOI: 10.3389/fnins.2021.732368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 08/09/2021] [Indexed: 11/24/2022] Open
Abstract
Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.
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Affiliation(s)
- Bryce A. Primavera
- National Institute of Standards and Technology, Boulder, CO, United States
- Department of Physics, University of Colorado Boulder, Boulder, CO, United States
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19
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Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry. SENSORS 2021; 21:s21093276. [PMID: 34068538 PMCID: PMC8125990 DOI: 10.3390/s21093276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/03/2021] [Accepted: 05/08/2021] [Indexed: 11/17/2022]
Abstract
The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.
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20
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Toomey E, Segall K, Castellani M, Colangelo M, Lynch N, Berggren KK. Superconducting Nanowire Spiking Element for Neural Networks. NANO LETTERS 2020; 20:8059-8066. [PMID: 32965119 DOI: 10.1021/acs.nanolett.0c03057] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ∼10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.
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Affiliation(s)
- E Toomey
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - K Segall
- Department of Physics and Astronomy, Colgate University, Hamilton, New York 13346, United States
| | - M Castellani
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - M Colangelo
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - N Lynch
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - K K Berggren
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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21
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Kim DW, Yi WS, Choi JY, Ashiba K, Baek JU, Jun HS, Kim JJ, Park JG. Double MgO-Based Perpendicular Magnetic Tunnel Junction for Artificial Neuron. Front Neurosci 2020; 14:309. [PMID: 32425744 PMCID: PMC7204637 DOI: 10.3389/fnins.2020.00309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/16/2020] [Indexed: 11/13/2022] Open
Abstract
A perpendicular spin transfer torque (p-STT)-based neuron was developed for a spiking neural network (SNN). It demonstrated the integration behavior of a typical neuron in an SNN; in particular, the integration behavior corresponding to magnetic resistance change gradually increased with the input spike number. This behavior occurred when the spin electron directions between double Co2Fe6B2 free and pinned layers in the p-STT-based neuron were switched from parallel to antiparallel states. In addition, a neuron circuit for integrate-and-fire operation was proposed. Finally, pattern-recognition simulation was performed for a single-layer SNN.
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Affiliation(s)
- Dong Won Kim
- Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, South Korea
| | - Woo Seok Yi
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Jin Young Choi
- MRAM Center, Department of Electronics and Computer Engineering, Hanyang University, Seoul, South Korea
| | - Kei Ashiba
- Wafer Engineering Department, SUMCO Corporation, Imari, Japan
| | - Jong Ung Baek
- Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, South Korea
| | - Han Sol Jun
- Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, South Korea
| | - Jae Joon Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Jea Gun Park
- Department of Nanoscale Semiconductor Engineering, Hanyang University, Seoul, South Korea.,Wafer Engineering Department, SUMCO Corporation, Imari, Japan
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22
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Bernert M, Yvert B. An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting. Int J Neural Syst 2019; 29:1850059. [DOI: 10.1142/s0129065718500594] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.
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Affiliation(s)
- Marie Bernert
- BrainTech Laboratory U1205, INSERM, 2280 Rue de la Piscine, 38400 Saint-Martin-d’Hères, France
- BrainTech Laboratory U1205, Université Grenoble Alpes, 2280 rue de la piscine, 38400 Saint-Martin-d’Hères, France
- LETI, CEA Grenoble, 17 Rue des Martyrs, 38000 Grenoble, France
| | - Blaise Yvert
- BrainTech Laboratory U1205, INSERM, 2280 Rue de la Piscine, 38400 Saint-Martin-d’Hères, France
- BrainTech Laboratory U1205, Université Grenoble Alpes, 2280 rue de la piscine, 38400 Saint-Martin-d’Hères, France
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23
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Abstract
In this paper, we present an electrical circuit of a leaky integrate-and-fire neuron with one VO2 switch, which models the properties of biological neurons. Based on VO2 neurons, a two-layer spiking neural network consisting of nine input and three output neurons is modeled in the SPICE simulator. The network contains excitatory and inhibitory couplings, and implements the winner-takes-all principle in pattern recognition. Using a supervised Spike-Timing-Dependent Plasticity training method and a timing method of information coding, the network was trained to recognize three patterns with dimensions of 3 × 3 pixels. The neural network is able to recognize up to 105 images per second, and has the potential to increase the recognition speed further.
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24
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Toomey E, Segall K, Berggren KK. Design of a Power Efficient Artificial Neuron Using Superconducting Nanowires. Front Neurosci 2019; 13:933. [PMID: 31551691 PMCID: PMC6738026 DOI: 10.3389/fnins.2019.00933] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 08/20/2019] [Indexed: 11/13/2022] Open
Abstract
With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently emerged as possible solutions. In particular, spiking neural networks (SNNs) offer a bio-realistic approach, relying on pulses, analogous to action potentials, as units of information. While software encoded networks provide flexibility and precision, they are often computationally expensive. As a result, hardware SNNs based on the spiking dynamics of a device or circuit represent an increasingly appealing direction. Here, we propose to use superconducting nanowires as a platform for the development of an artificial neuron. Building on an architecture first proposed for Josephson junctions, we rely on the intrinsic non-linearity of two coupled nanowires to generate spiking behavior, and use electrothermal circuit simulations to demonstrate that the nanowire neuron reproduces multiple characteristics of biological neurons. Furthermore, by harnessing the non-linearity of the superconducting nanowire's inductance, we develop a design for a variable inductive synapse capable of both excitatory and inhibitory control. We demonstrate that this synapse design supports direct fan-out, a feature that has been difficult to achieve in other superconducting architectures, and that the nanowire neuron's nominal energy performance is competitive with that of current technologies.
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Affiliation(s)
- Emily Toomey
- Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, United States
| | - Ken Segall
- Department of Physics and Astronomy, Colgate University, Hamilton, NY, United States
| | - Karl K. Berggren
- Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, United States
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25
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Spiking Neural Network Based on Cusp Catastrophe Theory. FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2019. [DOI: 10.2478/fcds-2019-0014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
This paper addresses the problem of effective processing using third generation neural networks. The article features two new models of spiking neurons based on the cusp catastrophe theory. The effectiveness of the models is demonstrated with an example of a network composed of three neurons solving the problem of linear inseparability of the XOR function. The proposed solutions are dedicated to hardware implementation using the Edge computing strategy. The paper presents simulation results and outlines further research direction in the field of practical applications and implementations using nanometer CMOS technologies and the current processing mode.
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26
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Xia Q, Yang JJ. Memristive crossbar arrays for brain-inspired computing. NATURE MATERIALS 2019; 18:309-323. [PMID: 30894760 DOI: 10.1038/s41563-019-0291-x] [Citation(s) in RCA: 428] [Impact Index Per Article: 85.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 02/16/2019] [Indexed: 05/19/2023]
Abstract
With their working mechanisms based on ion migration, the switching dynamics and electrical behaviour of memristive devices resemble those of synapses and neurons, making these devices promising candidates for brain-inspired computing. Built into large-scale crossbar arrays to form neural networks, they perform efficient in-memory computing with massive parallelism by directly using physical laws. The dynamical interactions between artificial synapses and neurons equip the networks with both supervised and unsupervised learning capabilities. Moreover, their ability to interface with analogue signals from sensors without analogue/digital conversions reduces the processing time and energy overhead. Although numerous simulations have indicated the potential of these networks for brain-inspired computing, experimental implementation of large-scale memristive arrays is still in its infancy. This Review looks at the progress, challenges and possible solutions for efficient brain-inspired computation with memristive implementations, both as accelerators for deep learning and as building blocks for spiking neural networks.
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Affiliation(s)
- Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
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27
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Wang Z, Rao M, Han JW, Zhang J, Lin P, Li Y, Li C, Song W, Asapu S, Midya R, Zhuo Y, Jiang H, Yoon JH, Upadhyay NK, Joshi S, Hu M, Strachan JP, Barnell M, Wu Q, Wu H, Qiu Q, Williams RS, Xia Q, Yang JJ. Capacitive neural network with neuro-transistors. Nat Commun 2018; 9:3208. [PMID: 30097585 PMCID: PMC6086838 DOI: 10.1038/s41467-018-05677-5] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 07/11/2018] [Indexed: 11/12/2022] Open
Abstract
Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with “leaky integrate-and-fire” dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals. Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.
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Affiliation(s)
- Zhongrui Wang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Mingyi Rao
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Jin-Woo Han
- Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA, 94035, USA
| | - Jiaming Zhang
- Hewlett-Packard Laboratories, Palo Alto, CA, 94304, USA
| | - Peng Lin
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Yunning Li
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Can Li
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Wenhao Song
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Shiva Asapu
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Rivu Midya
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Ye Zhuo
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Hao Jiang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Jung Ho Yoon
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Navnidhi Kumar Upadhyay
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Saumil Joshi
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA
| | - Miao Hu
- Hewlett-Packard Laboratories, Palo Alto, CA, 94304, USA
| | | | - Mark Barnell
- Air Force Research Lab, Information Directorate, Rome, NY, 13441, USA
| | - Qing Wu
- Air Force Research Lab, Information Directorate, Rome, NY, 13441, USA
| | - Huaqiang Wu
- Institute of Microelectronics, Tsinghua University, Beijing, 100084, China
| | - Qinru Qiu
- Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | | | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA.
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, 01003, USA.
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28
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Romeira B, Figueiredo JML, Javaloyes J. Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications. CHAOS (WOODBURY, N.Y.) 2017; 27:114323. [PMID: 29195310 DOI: 10.1063/1.5008888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.
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Affiliation(s)
- Bruno Romeira
- Centro de Electrónica, Optoelectrónica e Telecomunicações (CEOT), Departmento de Física, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
| | - José M L Figueiredo
- Centro de Electrónica, Optoelectrónica e Telecomunicações (CEOT), Departmento de Física, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
| | - Julien Javaloyes
- Departament de Física, Universitat de les Illes Balears, C/Valldemossa km 7.5, 07122 Palma de Mallorca, Spain
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