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Yan S, Meng Q, Xiao M, Wang Y, Lin Z. Sampling complex topology structures for spiking neural networks. Neural Netw 2024; 172:106121. [PMID: 38244355 DOI: 10.1016/j.neunet.2024.106121] [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: 05/11/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/22/2024]
Abstract
Spiking Neural Networks (SNNs) have been considered a potential competitor to Artificial Neural Networks (ANNs) due to their high biological plausibility and energy efficiency. However, the architecture design of SNN has not been well studied. Previous studies either use ANN architectures or directly search for SNN architectures under a highly constrained search space. In this paper, we aim to introduce much more complex connection topologies to SNNs to further exploit the potential of SNN architectures. To this end, we propose the topology-aware search space, which is the first search space that enables a more diverse and flexible design for both the spatial and temporal topology of the SNN architecture. Then, to efficiently obtain architecture from our search space, we propose the spatio-temporal topology sampling (STTS) algorithm. By leveraging the benefits of random sampling, STTS can yield powerful architecture without the need for an exhaustive search process, making it significantly more efficient than alternative search strategies. Extensive experiments on CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of our method. Notably, we obtain 70.79% top-1 accuracy on ImageNet with only 4 time steps, 1.79% higher than the second best model. Our code is available under https://github.com/stiger1000/Random-Sampling-SNN.
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Affiliation(s)
- Shen Yan
- Center for Data Science, Peking University, China.
| | - Qingyan Meng
- The Chinese University of Hong Kong, Shenzhen, China; Shenzhen Research Institute of Big Data, Shenzhen 518115, China.
| | - Mingqing Xiao
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, China.
| | - Yisen Wang
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, China; Institute for Artificial Intelligence, Peking University, China.
| | - Zhouchen Lin
- National Key Lab of General AI, School of Intelligence Science and Technology, Peking University, China; Institute for Artificial Intelligence, Peking University, China; Peng Cheng Laboratory, Shenzhen, 518055, China.
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2
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Shen G, Zhao D, Zeng Y. Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks. Neural Netw 2024; 170:190-201. [PMID: 37989040 DOI: 10.1016/j.neunet.2023.10.056] [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: 11/21/2022] [Revised: 08/22/2023] [Accepted: 10/31/2023] [Indexed: 11/23/2023]
Abstract
Inspired by the information transmission process in the brain, Spiking Neural Networks (SNNs) have gained considerable attention due to their event-driven nature. However, as the network structure grows complex, managing the spiking behavior within the network becomes challenging. Networks with excessively dense or sparse spikes fail to transmit sufficient information, inhibiting SNNs from exhibiting superior performance. Current SNNs linearly sum presynaptic information in postsynaptic neurons, overlooking the adaptive adjustment effect of dendrites on information processing. In this study, we introduce the Dendritic Spatial Gating Module (DSGM), which scales and translates the input, reducing the loss incurred when transforming the continuous membrane potential into discrete spikes. Simultaneously, by implementing the Dendritic Temporal Adjust Module (DTAM), dendrites assign different importance to inputs of different time steps, facilitating the establishment of the temporal dependency of spiking neurons and effectively integrating multi-step time information. The fusion of these two modules results in a more balanced spike representation within the network, significantly enhancing the neural network's performance. This approach has achieved state-of-the-art performance on static image datasets, including CIFAR10 and CIFAR100, as well as event datasets like DVS-CIFAR10, DVS-Gesture, and N-Caltech101. It also demonstrates competitive performance compared to the current state-of-the-art on the ImageNet dataset.
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Affiliation(s)
- Guobin Shen
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Dongcheng Zhao
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yi Zeng
- Brain-Inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS), Shanghai, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
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3
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Wu X, Song Y, Zhou Y, Jiang Y, Bai Y, Li X, Yang X. STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks. Front Neurosci 2023; 17:1261543. [PMID: 38027490 PMCID: PMC10667472 DOI: 10.3389/fnins.2023.1261543] [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: 07/20/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Spiking Neural Networks (SNNs) have shown great promise in processing spatio-temporal information compared to Artificial Neural Networks (ANNs). However, there remains a performance gap between SNNs and ANNs, which impedes the practical application of SNNs. With intrinsic event-triggered property and temporal dynamics, SNNs have the potential to effectively extract spatio-temporal features from event streams. To leverage the temporal potential of SNNs, we propose a self-attention-based temporal-channel joint attention SNN (STCA-SNN) with end-to-end training, which infers attention weights along both temporal and channel dimensions concurrently. It models global temporal and channel information correlations with self-attention, enabling the network to learn 'what' and 'when' to attend simultaneously. Our experimental results show that STCA-SNNs achieve better performance on N-MNIST (99.67%), CIFAR10-DVS (81.6%), and N-Caltech 101 (80.88%) compared with the state-of-the-art SNNs. Meanwhile, our ablation study demonstrates that STCA-SNNs improve the accuracy of event stream classification tasks.
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Affiliation(s)
| | - Yong Song
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Ya Zhou
- School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
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Aboumerhi K, Güemes A, Liu H, Tenore F, Etienne-Cummings R. Neuromorphic applications in medicine. J Neural Eng 2023; 20:041004. [PMID: 37531951 DOI: 10.1088/1741-2552/aceca3] [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: 02/20/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.
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Affiliation(s)
- Khaled Aboumerhi
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Amparo Güemes
- Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge CB3 0FA, United Kingdom
| | - Hongtao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Francesco Tenore
- Research and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
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5
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Wu Z, Shen Y, Zhang J, Liang H, Zhao R, Li H, Xiong J, Zhang X, Chua Y. BIDL: a brain-inspired deep learning framework for spatiotemporal processing. Front Neurosci 2023; 17:1213720. [PMID: 37564366 PMCID: PMC10410154 DOI: 10.3389/fnins.2023.1213720] [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: 04/28/2023] [Accepted: 06/22/2023] [Indexed: 08/12/2023] Open
Abstract
Brain-inspired deep spiking neural network (DSNN) which emulates the function of the biological brain provides an effective approach for event-stream spatiotemporal perception (STP), especially for dynamic vision sensor (DVS) signals. However, there is a lack of generalized learning frameworks that can handle various spatiotemporal modalities beyond event-stream, such as video clips and 3D imaging data. To provide a unified design flow for generalized spatiotemporal processing (STP) and to investigate the capability of lightweight STP processing via brain-inspired neural dynamics, this study introduces a training platform called brain-inspired deep learning (BIDL). This framework constructs deep neural networks, which leverage neural dynamics for processing temporal information and ensures high-accuracy spatial processing via artificial neural network layers. We conducted experiments involving various types of data, including video information processing, DVS information processing, 3D medical imaging classification, and natural language processing. These experiments demonstrate the efficiency of the proposed method. Moreover, as a research framework for researchers in the fields of neuroscience and machine learning, BIDL facilitates the exploration of different neural models and enables global-local co-learning. For easily fitting to neuromorphic chips and GPUs, the framework incorporates several optimizations, including iteration representation, state-aware computational graph, and built-in neural functions. This study presents a user-friendly and efficient DSNN builder for lightweight STP applications and has the potential to drive future advancements in bio-inspired research.
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Affiliation(s)
- Zhenzhi Wu
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Yangshu Shen
- Lynxi Technologies, Co. Ltd., Beijing, China
- Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China
| | - Jing Zhang
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Huaju Liang
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology (CNAEIT), Jiaxing, Zhejiang, China
| | | | - Han Li
- Lynxi Technologies, Co. Ltd., Beijing, China
| | - Jianping Xiong
- Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China
| | - Xiyu Zhang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yansong Chua
- Neuromorphic Computing Laboratory, China Nanhu Academy of Electronics and Information Technology (CNAEIT), Jiaxing, Zhejiang, China
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Yu C, Gu Z, Li D, Wang G, Wang A, Li E. STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks. Front Neurosci 2022; 16:1079357. [PMID: 36620452 PMCID: PMC9817103 DOI: 10.3389/fnins.2022.1079357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/08/2022] [Indexed: 12/25/2022] Open
Abstract
Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing event streams. However, existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither of which can extract temporal dependencies adequately. In this work, we take inspiration from biological synapses and propose a Spatio-Temporal Synaptic Connection SNN (STSC-SNN) model to enhance the spatio-temporal receptive fields of synaptic connections, thereby establishing temporal dependencies across layers. Specifically, we incorporate temporal convolution and attention mechanisms to implement synaptic filtering and gating functions. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks. In addition, we investigate the impact of performance via varied spatial-temporal receptive fields and reevaluate the temporal modules in SNNs. Our approach is tested on neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST, CIFAR10-DVS (image classification), and SHD (speech digit recognition). The results show that the proposed model outperforms the state-of-the-art accuracy on nearly all datasets.
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Affiliation(s)
- Chengting Yu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Zheming Gu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Da Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Gaoang Wang
- Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
| | - Aili Wang
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China,*Correspondence: Aili Wang ✉
| | - Erping Li
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China,Zhejiang University - University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, China
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Zhao Z, Wang Y, Zou Q, Xu T, Tao F, Zhang J, Wang X, Shi CJR, Luo J, Xie Y. The spike gating flow: A hierarchical structure-based spiking neural network for online gesture recognition. Front Neurosci 2022; 16:923587. [PMID: 36408382 PMCID: PMC9667043 DOI: 10.3389/fnins.2022.923587] [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: 04/19/2022] [Accepted: 10/03/2022] [Indexed: 01/25/2023] Open
Abstract
Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in emerging industrial fields such as robotic visions and automobiles. However, current deep learning (DL) faces major challenges for such applications because of the huge computational cost and inefficient learning. Hence, we developed a novel brain-inspired spiking neural network (SNN) based system titled spiking gating flow (SGF) for online action learning. The developed system consists of multiple SGF units which are assembled in a hierarchical manner. A single SGF unit contains three layers: a feature extraction layer, an event-driven layer, and a histogram-based training layer. To demonstrate the capability of the developed system, we employed a standard dynamic vision sensor (DVS) gesture classification as a benchmark. The results indicated that we can achieve 87.5% of accuracy which is comparable with DL, but at a smaller training/inference data number ratio of 1.5:1. Only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation based SNNs. Finally, we conclude the few-shot learning (FSL) paradigm of the developed network: 1) a hierarchical structure-based network design involves prior human knowledge; 2) SNNs for content-based global dynamic feature detection.
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Affiliation(s)
- Zihao Zhao
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Yanhong Wang
- School of Microelectronics, Fudan University, Shanghai, China,Alibaba DAMO Academy, Shanghai, China
| | - Qiaosha Zou
- School of Microelectronics, Fudan University, Shanghai, China
| | - Tie Xu
- Alibaba Group, Hangzhou, China
| | | | | | - Xiaoan Wang
- BrainUp Research Laboratory, Shanghai, China
| | - C.-J. Richard Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - Junwen Luo
- Alibaba DAMO Academy, Shanghai, China,BrainUp Research Laboratory, Shanghai, China,*Correspondence: Junwen Luo
| | - Yuan Xie
- Alibaba DAMO Academy, Shanghai, China
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Wu Z, Zhang H, Lin Y, Li G, Wang M, Tang Y. LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:6249-6262. [PMID: 33979292 DOI: 10.1109/tnnls.2021.3073016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the bioplausible neuronal dynamics and simplicity, LIF-SNN benefits from event-driven processing, however, usually face the embarrassment of reduced performance. This may because, in LIF-SNN, the neurons transmit information via spikes. To address this issue, in this work, we propose a leaky integrate and analog fire (LIAF) neuron model so that analog values can be transmitted among neurons, and a deep network termed LIAF-Net is built on it for efficient spatiotemporal processing. In the temporal domain, LIAF follows the traditional LIF dynamics to maintain its temporal processing capability. In the spatial domain, LIAF is able to integrate spatial information through convolutional integration or fully connected integration. As a spatiotemporal layer, LIAF can also be used with traditional artificial neural network (ANN) layers jointly. In addition, the built network can be trained with backpropagation through time (BPTT) directly, which avoids the performance loss caused by ANN to SNN conversion. Experiment results indicate that LIAF-Net achieves comparable performance to the gated recurrent unit (GRU) and long short-term memory (LSTM) on bAbI question answering (QA) tasks and achieves state-of-the-art performance on spatiotemporal dynamic vision sensor (DVS) data sets, including MNIST-DVS, CIFAR10-DVS, and DVS128 Gesture, with much less number of synaptic weights and computational overhead compared with traditional networks built by LSTM, GRU, convolutional LSTM (ConvLSTM), or 3-D convolution (Conv3D). Compared with traditional LIF-SNN, LIAF-Net also shows dramatic accuracy gain on all these experiments. In conclusion, LIAF-Net provides a framework combining the advantages of both ANNs and SNNs for lightweight and efficient spatiotemporal information processing.
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Go S, Wang Q, Wang B, Jiang Y, Bajalovic N, Loke DK. Continual Learning Electrical Conduction in Resistive‐Switching‐Memory Materials. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Shao‐Xiang Go
- Department of Science, Mathematics and Technology Singapore University of Technology and Design 487372 Singapore
| | - Qiang Wang
- Department of Science, Mathematics and Technology Singapore University of Technology and Design 487372 Singapore
| | - Bo Wang
- Department of Information Systems Technology and Design Singapore University of Technology and Design 487372 Singapore
| | - Yu Jiang
- Department of Science, Mathematics and Technology Singapore University of Technology and Design 487372 Singapore
| | - Natasa Bajalovic
- Department of Science, Mathematics and Technology Singapore University of Technology and Design 487372 Singapore
| | - Desmond K. Loke
- Department of Science, Mathematics and Technology Singapore University of Technology and Design 487372 Singapore
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Wu D, Yi X, Huang X. A Little Energy Goes a Long Way: Build an Energy-Efficient, Accurate Spiking Neural Network From Convolutional Neural Network. Front Neurosci 2022; 16:759900. [PMID: 35692427 PMCID: PMC9179229 DOI: 10.3389/fnins.2022.759900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/28/2022] [Indexed: 11/13/2022] Open
Abstract
This article conforms to a recent trend of developing an energy-efficient Spiking Neural Network (SNN), which takes advantage of the sophisticated training regime of Convolutional Neural Network (CNN) and converts a well-trained CNN to an SNN. We observe that the existing CNN-to-SNN conversion algorithms may keep a certain amount of residual current in the spiking neurons in SNN, and the residual current may cause significant accuracy loss when inference time is short. To deal with this, we propose a unified framework to equalize the output of the convolutional or dense layer in CNN and the accumulated current in SNN, and maximally align the spiking rate of a neuron with its corresponding charge. This framework enables us to design a novel explicit current control (ECC) method for the CNN-to-SNN conversion which considers multiple objectives at the same time during the conversion, including accuracy, latency, and energy efficiency. We conduct an extensive set of experiments on different neural network architectures, e.g., VGG, ResNet, and DenseNet, to evaluate the resulting SNNs. The benchmark datasets include not only the image datasets such as CIFAR-10/100 and ImageNet but also the Dynamic Vision Sensor (DVS) image datasets such as DVS-CIFAR-10. The experimental results show the superior performance of our ECC method over the state-of-the-art.
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Affiliation(s)
- Dengyu Wu
- Department of Computer Science, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Dengyu Wu
| | - Xinping Yi
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom
| | - Xiaowei Huang
- Department of Computer Science, University of Liverpool, Liverpool, United Kingdom
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Javanshir A, Nguyen TT, Mahmud MAP, Kouzani AZ. Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks. Neural Comput 2022; 34:1289-1328. [PMID: 35534005 DOI: 10.1162/neco_a_01499] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 01/18/2022] [Indexed: 11/04/2022]
Abstract
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.
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Affiliation(s)
| | - Thanh Thi Nguyen
- School of Information Technology, Deakin University (Burwood Campus) Burwood, VIC 3125, Australia
| | - M A Parvez Mahmud
- School of Engineering, Deakin University, Geelong, VIC 3216, Australia
| | - Abbas Z Kouzani
- School of Engineering, Deakin University, Geelong, VIC 3216, Australia
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Wu Z, Zhang Z, Gao H, Qin J, Zhao R, Zhao G, Li G. Modeling learnable electrical synapse for high precision spatio-temporal recognition. Neural Netw 2022; 149:184-194. [DOI: 10.1016/j.neunet.2022.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 11/30/2021] [Accepted: 02/06/2022] [Indexed: 10/19/2022]
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13
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Datta S, Boulgouris NV. Recognition of grammatical class of imagined words from EEG signals using convolutional neural network. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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14
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Lee SH, Park HL, Kim MH, Kim MH, Park BG, Lee SD. Realization of Biomimetic Synaptic Functions in a One-Cell Organic Resistive Switching Device Using the Diffusive Parameter of Conductive Filaments. ACS APPLIED MATERIALS & INTERFACES 2020; 12:51719-51728. [PMID: 33151051 DOI: 10.1021/acsami.0c15519] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Toward the successful development of artificial intelligence, artificial synapses based on resistive switching devices are essential ingredients to perform information processing in spiking neural networks. In neural processes, synaptic plasticity related to the history of neuron activity plays a critical role during learning. In resistive switching devices, it is barely possible to emulate both short-term plasticity and long-term plasticity due to the uncontrollable dynamics of the conductive filaments (CFs). Despite extensive effort to realize synaptic plasticity in such devices, it is still challenging to achieve reliable synaptic functions due to the overgrowth of CFs in a random fashion. Herein, we propose an organic resistive switching device with bio-realistic synaptic functions by adjusting the CF diffusive parameter. In the proposed device, complete synaptic plasticity provides the history-dependent change in the conductance. Moreover, the homeostatic feedback, which resembles the biological process, regulates CF growth in our device, which enhances the reliability of synaptic plasticity. This novel concept for realizing synaptic functions in organic resistive switching devices may provide a physical platform to advance the fundamental understanding of learning and memory mechanisms and develop a variety of neural circuits and neuromorphic systems that can be linked to artificial intelligence and next-generation computing paradigm.
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Affiliation(s)
- Sin-Hyung Lee
- School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 702-701, Republic of Korea
| | - Hea-Lim Park
- Department of Materials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Min-Hoi Kim
- Department of Creative Convergence Engineering, Hanbat National University, Yuseong-gu, Daejeon 305-719, Republic of Korea
| | - Min-Hwi Kim
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Byung-Gook Park
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sin-Doo Lee
- School of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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