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Ma D, Jin X, Sun S, Li Y, Wu X, Hu Y, Yang F, Tang H, Zhu X, Lin P, Pan G. Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning. Natl Sci Rev 2024; 11:nwae102. [PMID: 38689713 PMCID: PMC11060491 DOI: 10.1093/nsr/nwae102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/03/2024] [Accepted: 02/23/2024] [Indexed: 05/02/2024] Open
Abstract
Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.
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Affiliation(s)
- De Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310027, China
| | - Xiaofei Jin
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
| | - Shichun Sun
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
| | - Yitao Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China
| | - Xundong Wu
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
| | - Youneng Hu
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Fangchao Yang
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310027, China
| | - Xiaolei Zhu
- College of Micro-Nano College of Micro-Nano Electronics, Zhejiang University, Hangzhou 311200, China
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
| | - Peng Lin
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310027, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
- Research Center for Intelligent Computing Hardware, Zhejiang Lab, Hangzhou 311121, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou 310027, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University, Hangzhou 310027, China
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Duan X, Cao Z, Gao K, Yan W, Sun S, Zhou G, Wu Z, Ren F, Sun B. Memristor-Based Neuromorphic Chips. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2310704. [PMID: 38168750 DOI: 10.1002/adma.202310704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/15/2023] [Indexed: 01/05/2024]
Abstract
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.
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Affiliation(s)
- Xuegang Duan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kaikai Gao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Wentao Yan
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Siyu Sun
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing, 400715, China
| | - Zhenhua Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, 800 DongChuan Rd, Shanghai, 200240, China
| | - Fenggang Ren
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of hepatobiliary surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
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3
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Park TJ, Deng S, Manna S, Islam ANMN, Yu H, Yuan Y, Fong DD, Chubykin AA, Sengupta A, Sankaranarayanan SKRS, Ramanathan S. Complex Oxides for Brain-Inspired Computing: A Review. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2203352. [PMID: 35723973 DOI: 10.1002/adma.202203352] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/02/2022] [Indexed: 06/15/2023]
Abstract
The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.
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Affiliation(s)
- Tae Joon Park
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Sunbin Deng
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Sukriti Manna
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA
| | - A N M Nafiul Islam
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Haoming Yu
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Yifan Yuan
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Dillon D Fong
- Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Alexander A Chubykin
- Department of Biological Sciences, Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, 47907, USA
| | - Abhronil Sengupta
- Department of Electrical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Subramanian K R S Sankaranarayanan
- Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL, 60439, USA
- Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL, 60607, USA
| | - Shriram Ramanathan
- School of Materials Engineering, Purdue University, West Lafayette, IN, 47907, USA
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Yang G, Lee W, Seo Y, Lee C, Seok W, Park J, Sim D, Park C. Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons. SENSORS (BASEL, SWITZERLAND) 2023; 23:7232. [PMID: 37631767 PMCID: PMC10459513 DOI: 10.3390/s23167232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/23/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models.
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Affiliation(s)
- Geunbo Yang
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (G.Y.); (Y.S.); (C.L.)
| | - Wongyu Lee
- Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (W.L.); (W.S.)
| | - Youjung Seo
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (G.Y.); (Y.S.); (C.L.)
| | - Choongseop Lee
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (G.Y.); (Y.S.); (C.L.)
| | - Woojoon Seok
- Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (W.L.); (W.S.)
| | - Jongkil Park
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea;
| | - Donggyu Sim
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (G.Y.); (Y.S.); (C.L.)
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea; (G.Y.); (Y.S.); (C.L.)
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5
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Gautam A, Kohno T. Adaptive STDP-based on-chip spike pattern detection. Front Neurosci 2023; 17:1203956. [PMID: 37521704 PMCID: PMC10374023 DOI: 10.3389/fnins.2023.1203956] [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: 04/11/2023] [Accepted: 06/15/2023] [Indexed: 08/01/2023] Open
Abstract
A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.
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Research Progress of spiking neural network in image classification: a review. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04553-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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7
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Gao H, He J, Wang H, Wang T, Zhong Z, Yu J, Wang Y, Tian M, Shi C. High-accuracy deep ANN-to-SNN conversion using quantization-aware training framework and calcium-gated bipolar leaky integrate and fire neuron. Front Neurosci 2023; 17:1141701. [PMID: 36968504 PMCID: PMC10030499 DOI: 10.3389/fnins.2023.1141701] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/07/2023] [Indexed: 03/29/2023] Open
Abstract
Spiking neural networks (SNNs) have attracted intensive attention due to the efficient event-driven computing paradigm. Among SNN training methods, the ANN-to-SNN conversion is usually regarded to achieve state-of-the-art recognition accuracies. However, many existing ANN-to-SNN techniques impose lengthy post-conversion steps like threshold balancing and weight renormalization, to compensate for the inherent behavioral discrepancy between artificial and spiking neurons. In addition, they require a long temporal window to encode and process as many spikes as possible to better approximate the real-valued ANN neurons, leading to a high inference latency. To overcome these challenges, we propose a calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model to better approximate the functions of the ReLU neurons widely adopted in ANNs. We also propose a quantization-aware training (QAT)-based framework leveraging an off-the-shelf QAT toolkit for easy ANN-to-SNN conversion, which directly exports the learned ANN weights to SNNs requiring no post-conversion processing. We benchmarked our method on typical deep network structures with varying time-step lengths from 8 to 128. Compared to other research, our converted SNNs reported competitively high-accuracy performance, while enjoying relatively short inference time steps.
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Affiliation(s)
- Haoran Gao
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Junxian He
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Haibing Wang
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Tengxiao Wang
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Zhengqing Zhong
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Jianyi Yu
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Ying Wang
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Min Tian
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Cong Shi
- The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
- *Correspondence: Cong Shi
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Forno E, Fra V, Pignari R, Macii E, Urgese G. Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task. Front Neurosci 2022; 16:999029. [PMID: 36620463 PMCID: PMC9811205 DOI: 10.3389/fnins.2022.999029] [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: 07/20/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Spiking Neural Networks (SNNs), known for their potential to enable low energy consumption and computational cost, can bring significant advantages to the realm of embedded machine learning for edge applications. However, input coming from standard digital sensors must be encoded into spike trains before it can be elaborated with neuromorphic computing technologies. We present here a detailed comparison of available spike encoding techniques for the translation of time-varying signals into the event-based signal domain, tested on two different datasets both acquired through commercially available digital devices: the Free Spoken Digit dataset (FSD), consisting of 8-kHz audio files, and the WISDM dataset, composed of 20-Hz recordings of human activity through mobile and wearable inertial sensors. We propose a complete pipeline to benchmark these encoding techniques by performing time-dependent signal classification through a Spiking Convolutional Neural Network (sCNN), including a signal preprocessing step consisting of a bank of filters inspired by the human cochlea, feature extraction by production of a sonogram, transfer learning via an equivalent ANN, and model compression schemes aimed at resource optimization. The resulting performance comparison and analysis provides a powerful practical tool, empowering developers to select the most suitable coding method based on the type of data and the desired processing algorithms, and further expands the applicability of neuromorphic computational paradigms to embedded sensor systems widely employed in the IoT and industrial domains.
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Amiri M, Jafari AH, Makkiabadi B, Nazari S. A Novel Unsupervised Spatial–Temporal Learning Mechanism in a Bio-inspired Spiking Neural Network. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10097-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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10
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Garg N, Balafrej I, Stewart TC, Portal JM, Bocquet M, Querlioz D, Drouin D, Rouat J, Beilliard Y, Alibart F. Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential. Front Neurosci 2022; 16:983950. [PMID: 36340782 PMCID: PMC9634260 DOI: 10.3389/fnins.2022.983950] [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: 07/01/2022] [Accepted: 09/05/2022] [Indexed: 11/27/2022] Open
Abstract
This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb’s plasticity mechanism on neuromorphic hardware. The proposed VDSP learning rule updates the synaptic conductance on the spike of the postsynaptic neuron only, which reduces by a factor of two the number of updates with respect to standard spike timing dependent plasticity (STDP). This update is dependent on the membrane potential of the presynaptic neuron, which is readily available as part of neuron implementation and hence does not require additional memory for storage. Moreover, the update is also regularized on synaptic weight and prevents explosion or vanishing of weights on repeated stimulation. Rigorous mathematical analysis is performed to draw an equivalence between VDSP and STDP. To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits. We report 85.01 ± 0.76% (Mean ± SD) accuracy for a network of 100 output neurons on the MNIST dataset. The performance improves when scaling the network size (89.93 ± 0.41% for 400 output neurons, 90.56 ± 0.27 for 500 neurons), which validates the applicability of the proposed learning rule for spatial pattern recognition tasks. Future work will consider more complicated tasks. Interestingly, the learning rule better adapts than STDP to the frequency of input signal and does not require hand-tuning of hyperparameters.
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Affiliation(s)
- Nikhil Garg
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
- Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve-d’Ascq, France
- *Correspondence: Nikhil Garg,
| | - Ismael Balafrej
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
- NECOTIS Research Lab, Department of Electrical and Computer Engineering, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Terrence C. Stewart
- National Research Council Canada, University of Waterloo Collaboration Centre, Waterloo, ON, Canada
| | - Jean-Michel Portal
- Aix-Marseille Université, Université de Toulon, CNRS, IM2NP, Marseille, France
| | - Marc Bocquet
- Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve-d’Ascq, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Dominique Drouin
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jean Rouat
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
- NECOTIS Research Lab, Department of Electrical and Computer Engineering, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Yann Beilliard
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Fabien Alibart
- Institut Interdisciplinaire d’Innovation Technologique (3IT), Université de Sherbrooke, Sherbrooke, QC, Canada
- Laboratoire Nanotechnologies Nanosystèmes (LN2)—CNRS UMI-3463, Université de Sherbrooke, Sherbrooke, QC, Canada
- Institute of Electronics, Microelectronics and Nanotechnology (IEMN), Université de Lille, Villeneuve-d’Ascq, France
- Fabien Alibart,
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11
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Payvand M, Moro F, Nomura K, Dalgaty T, Vianello E, Nishi Y, Indiveri G. Self-organization of an inhomogeneous memristive hardware for sequence learning. Nat Commun 2022; 13:5793. [PMID: 36184665 PMCID: PMC9527242 DOI: 10.1038/s41467-022-33476-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 09/19/2022] [Indexed: 11/27/2022] Open
Abstract
Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible” learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware. One gap between the neuro-inspired computing and its applications lies in the intrinsic variability of the devices. Here, Payvand et al. suggest a technologically plausible co-design of the hardware architecture which takes into account and exploits the physics behind memristors.
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Affiliation(s)
- Melika Payvand
- Institute for Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Filippo Moro
- Institute for Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Université Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - Kumiko Nomura
- Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan
| | - Thomas Dalgaty
- Université Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - Elisa Vianello
- Université Grenoble Alpes, CEA, Leti, F-38000, Grenoble, France
| | - Yoshifumi Nishi
- Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan
| | - Giacomo Indiveri
- Institute for Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
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12
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Comsa IM, Potempa K, Versari L, Fischbacher T, Gesmundo A, Alakuijala J. Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5939-5952. [PMID: 33900924 DOI: 10.1109/tnnls.2021.3071976] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically plausible synaptic transfer function. In addition, we use trainable pulses that provide bias, add flexibility during training, and exploit the decayed part of the synaptic function. We show that such networks can be successfully trained on multiple data sets encoded in time, including MNIST. Our model outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. The spiking network spontaneously discovers two operating modes, mirroring the accuracy-speed tradeoff observed in human decision-making: a highly accurate but slow regime, and a fast but slightly lower accuracy regime. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks toward energy-efficient, state-based biologically inspired neural architectures. We provide open-source code for the model.
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13
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Wang Z, Liu J, Ma Y, Chen B, Zheng N, Ren P. Perturbation of Spike Timing Benefits Neural Network Performance on Similarity Search. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:4361-4372. [PMID: 33606643 DOI: 10.1109/tnnls.2021.3056694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space and network parameters. It can consistently increase the agent's exploration ability and lead to richer sets of behaviors. Evolutionary strategies also apply parameter perturbations, which makes network architecture robust and diverse. Our main concern is whether the notion of synaptic perturbation introduced in a spiking neural network (SNN) is biologically relevant or if novel frameworks and components are desired to account for the perturbation properties of artificial neural systems. In this work, we first review part of the locality-sensitive hashing (LSH) of similarity search, the FLY algorithm, as recently published in Science, and propose an improved architecture, time-shifted spiking LSH (TS-SLSH), with the consideration of temporal perturbations of the firing moments of spike pulses. Experiment results show promising performance of the proposed method and demonstrate its generality to various spiking neuron models. Therefore, we expect temporal perturbation to play an active role in SNN performance.
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14
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Wang H, He Z, Wang T, He J, Zhou X, Wang Y, Liu L, Wu N, Tian M, Shi C. TripleBrain: A Compact Neuromorphic Hardware Core With Fast On-Chip Self-Organizing and Reinforcement Spike-Timing Dependent Plasticity. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:636-650. [PMID: 35802542 DOI: 10.1109/tbcas.2022.3189240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Human brain cortex acts as a rich inspiration source for constructing efficient artificial cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired computing paradigms for compact, fast and high-accuracy neuromorphic hardware implementation. We propose the TripleBrain hardware core that tightly combines three common brain-inspired factors: the spike-based processing and plasticity, the self-organizing map (SOM) mechanism and the reinforcement learning scheme, to improve object recognition accuracy and processing throughput, while keeping low resource costs. The proposed hardware core is fully event-driven to mitigate unnecessary operations, and enables various on-chip learning rules (including the proposed SOM-STDP & R-STDP rule and the R-SOM-STDP rule regarded as the two variants of our TripleBrain learning rule) with different accuracy-latency tradeoffs to satisfy user requirements. An FPGA prototype of the neuromorphic core was implemented and elaborately tested. It realized high-speed learning (1349 frame/s) and inference (2698 frame/s), and obtained comparably high recognition accuracies of 95.10%, 80.89%, 100%, 94.94%, 82.32%, 100% and 97.93% on the MNIST, ETH-80, ORL-10, Yale-10, N-MNIST, Poker-DVS and Posture-DVS datasets, respectively, while only consuming 4146 (7.59%) slices, 32 (3.56%) DSPs and 131 (24.04%) Block RAMs on a Xilinx Zynq-7045 FPGA chip. Our neuromorphic core is very attractive for real-time resource-limited edge intelligent systems.
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15
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Yu Q, Li S, Tang H, Wang L, Dang J, Tan KC. Toward Efficient Processing and Learning With Spikes: New Approaches for Multispike Learning. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:1364-1376. [PMID: 32356771 DOI: 10.1109/tcyb.2020.2984888] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the biological systems, whose efficiency attracts increasing attentions to the field of neuromorphic computing. However, efficient processing and learning of discrete spikes still remain a challenging problem. In this article, we make our contributions toward this direction. A simplified spiking neuron model is first introduced with the effects of both synaptic input and firing output on the membrane potential being modeled with an impulse function. An event-driven scheme is then presented to further improve the processing efficiency. Based on the neuron model, we propose two new multispike learning rules which demonstrate better performance over other baselines on various tasks, including association, classification, and feature detection. In addition to efficiency, our learning rules demonstrate high robustness against the strong noise of different types. They can also be generalized to different spike coding schemes for the classification task, and notably, the single neuron is capable of solving multicategory classifications with our learning rules. In the feature detection task, we re-examine the ability of unsupervised spike-timing-dependent plasticity with its limitations being presented, and find a new phenomenon of losing selectivity. In contrast, our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied. Moreover, our rules cannot only detect features but also discriminate them. The improved performance of our methods would contribute to neuromorphic computing as a preferable choice.
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Büchel J, Zendrikov D, Solinas S, Indiveri G, Muir DR. Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors. Sci Rep 2021; 11:23376. [PMID: 34862429 PMCID: PMC8642544 DOI: 10.1038/s41598-021-02779-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/22/2021] [Indexed: 11/14/2022] Open
Abstract
Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog circuits are sensitive to process-induced variation among transistors in a chip ("device mismatch"). For neuromorphic implementation of Spiking Neural Networks (SNNs), mismatch causes parameter variation between identically-configured neurons and synapses. Each chip exhibits a different distribution of neural parameters, causing deployed networks to respond differently between chips. Current solutions to mitigate mismatch based on per-chip calibration or on-chip learning entail increased design complexity, area and cost, making deployment of neuromorphic devices expensive and difficult. Here we present a supervised learning approach that produces SNNs with high robustness to mismatch and other common sources of noise. Our method trains SNNs to perform temporal classification tasks by mimicking a pre-trained dynamical system, using a local learning rule from non-linear control theory. We demonstrate our method on two tasks requiring temporal memory, and measure the robustness of our approach to several forms of noise and mismatch. We show that our approach is more robust than common alternatives for training SNNs. Our method provides robust deployment of pre-trained networks on mixed-signal neuromorphic hardware, without requiring per-device training or calibration.
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Affiliation(s)
- Julian Büchel
- SynSense, Thurgauerstrasse 40, 8050, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Dmitrii Zendrikov
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Sergio Solinas
- Department of Biomedical Science, University of Sassari, Piazza Università, 21, 07100, Sassari, Sardegna, Italy
| | - Giacomo Indiveri
- SynSense, Thurgauerstrasse 40, 8050, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland
| | - Dylan R Muir
- SynSense, Thurgauerstrasse 40, 8050, Zurich, Switzerland.
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Nishi Y, Nomura K, Marukame T, Mizushima K. Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity. Sci Rep 2021; 11:18282. [PMID: 34521895 PMCID: PMC8440757 DOI: 10.1038/s41598-021-97583-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
Spike timing-dependent plasticity (STDP), which is widely studied as a fundamental synaptic update rule for neuromorphic hardware, requires precise control of continuous weights. From the viewpoint of hardware implementation, a simplified update rule is desirable. Although simplified STDP with stochastic binary synapses was proposed previously, we find that it leads to degradation of memory maintenance during learning, which is unfavourable for unsupervised online learning. In this work, we propose a stochastic binary synaptic model where the cumulative probability of the weight change evolves in a sigmoidal fashion with potentiation or depression trials, which can be implemented using a pair of switching devices consisting of serially connected multiple binary memristors. As a benchmark test we perform simulations of unsupervised learning of MNIST images with a two-layer network and show that simplified STDP in combination with this model can outperform conventional rules with continuous weights not only in memory maintenance but also in recognition accuracy. Our method achieves 97.3% in recognition accuracy, which is higher than that reported with standard STDP in the same framework. We also show that the high performance of our learning rule is robust against device-to-device variability of the memristor's probabilistic behaviour.
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Affiliation(s)
- Yoshifumi Nishi
- Frontier Research Laboratory, Corporate R&D Center, Toshiba Corporation, 1, Komukai-Toshiba-Cho, Saiwai-ku, Kawasaki, 212-8582, Japan.
| | - Kumiko Nomura
- Frontier Research Laboratory, Corporate R&D Center, Toshiba Corporation, 1, Komukai-Toshiba-Cho, Saiwai-ku, Kawasaki, 212-8582, Japan
| | - Takao Marukame
- Frontier Research Laboratory, Corporate R&D Center, Toshiba Corporation, 1, Komukai-Toshiba-Cho, Saiwai-ku, Kawasaki, 212-8582, Japan
| | - Koichi Mizushima
- Frontier Research Laboratory, Corporate R&D Center, Toshiba Corporation, 1, Komukai-Toshiba-Cho, Saiwai-ku, Kawasaki, 212-8582, Japan
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18
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Gangopadhyay A, Chakrabartty S. A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons. Front Neurosci 2021; 15:715451. [PMID: 34393719 PMCID: PMC8355563 DOI: 10.3389/fnins.2021.715451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. In this paper we describe a backpropagation-less learning approach to train a network of spiking GT neurons by enforcing sparsity constraints on the overall network spiking activity. The key features of the model and the proposed learning framework are: (a) spike responses are generated as a result of constraint violation and hence can be viewed as Lagrangian parameters; (b) the optimal parameters for a given task can be learned using neurally relevant local learning rules and in an online manner; (c) the network optimizes itself to encode the solution with as few spikes as possible (sparsity); (d) the network optimizes itself to operate at a solution with the maximum dynamic range and away from saturation; and (e) the framework is flexible enough to incorporate additional structural and connectivity constraints on the network. As a result, the proposed formulation is attractive for designing neuromorphic tinyML systems that are constrained in energy, resources, and network structure. In this paper, we show how the approach could be used for unsupervised and supervised learning such that minimizing a training error is equivalent to minimizing the overall spiking activity across the network. We then build on this framework to implement three different multi-layer spiking network architectures with progressively increasing flexibility in training and consequently, sparsity. We demonstrate the applicability of the proposed algorithm for resource-efficient learning using a publicly available machine olfaction dataset with unique challenges like sensor drift and a wide range of stimulus concentrations. In all of these case studies we show that a GT network trained using the proposed learning approach is able to minimize the network-level spiking activity while producing classification accuracy that are comparable to standard approaches on the same dataset.
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Affiliation(s)
| | - Shantanu Chakrabartty
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States
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19
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Auge D, Hille J, Mueller E, Knoll A. A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10562-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.
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20
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Hu SG, Qiao GC, Chen TP, Yu Q, Liu Y, Rong LM. Quantized STDP-based online-learning spiking neural network. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05832-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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21
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Stapmanns J, Hahne J, Helias M, Bolten M, Diesmann M, Dahmen D. Event-Based Update of Synapses in Voltage-Based Learning Rules. Front Neuroinform 2021; 15:609147. [PMID: 34177505 PMCID: PMC8222618 DOI: 10.3389/fninf.2021.609147] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 04/07/2021] [Indexed: 11/13/2022] Open
Abstract
Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. In some learning rules membrane potentials not only influence synaptic weight changes at the time points of spike events but in a continuous manner. In these cases, synapses therefore require information on the full time course of membrane potentials to update their strength which a priori suggests a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.
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Affiliation(s)
- Jonas Stapmanns
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Jan Hahne
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Moritz Helias
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Institute for Theoretical Solid State Physics, RWTH Aachen University, Aachen, Germany
| | - Matthias Bolten
- School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
- Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - David Dahmen
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure Function Relationship (INM-10), Jülich Research Centre, Jülich, Germany
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22
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Covi E, Donati E, Liang X, Kappel D, Heidari H, Payvand M, Wang W. Adaptive Extreme Edge Computing for Wearable Devices. Front Neurosci 2021; 15:611300. [PMID: 34045939 PMCID: PMC8144334 DOI: 10.3389/fnins.2021.611300] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 03/24/2021] [Indexed: 11/13/2022] Open
Abstract
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions toward smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g., memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.
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Affiliation(s)
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Xiangpeng Liang
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - David Kappel
- Bernstein Center for Computational Neuroscience, III Physikalisches Institut–Biophysik, Georg-August Universität, Göttingen, Germany
| | - Hadi Heidari
- Microelectronics Lab, James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, Eidgenössische Technische Hochschule Zürich (ETHZ), Zurich, Switzerland
| | - Wei Wang
- The Andrew and Erna Viterbi Department of Electrical Engineering, Technion–Israel Institute of Technology, Haifa, Israel
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Syed T, Kakani V, Cui X, Kim H. Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms. SENSORS (BASEL, SWITZERLAND) 2021; 21:3240. [PMID: 34067080 PMCID: PMC8125750 DOI: 10.3390/s21093240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/29/2021] [Accepted: 05/02/2021] [Indexed: 11/16/2022]
Abstract
In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset.
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Affiliation(s)
- Tehreem Syed
- Electrical and Computer Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea;
| | - Vijay Kakani
- Integrated System and Engineering, School of Global Convergence Studies, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea;
| | - Xuenan Cui
- Information and Communication Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea;
| | - Hakil Kim
- Electrical and Computer Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 22212, Korea;
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24
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Subbulakshmi Radhakrishnan S, Sebastian A, Oberoi A, Das S, Das S. A biomimetic neural encoder for spiking neural network. Nat Commun 2021; 12:2143. [PMID: 33837210 PMCID: PMC8035177 DOI: 10.1038/s41467-021-22332-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 03/09/2021] [Indexed: 02/07/2023] Open
Abstract
Spiking neural networks (SNNs) promise to bridge the gap between artificial neural networks (ANNs) and biological neural networks (BNNs) by exploiting biologically plausible neurons that offer faster inference, lower energy expenditure, and event-driven information processing capabilities. However, implementation of SNNs in future neuromorphic hardware requires hardware encoders analogous to the sensory neurons, which convert external/internal stimulus into spike trains based on specific neural algorithm along with inherent stochasticity. Unfortunately, conventional solid-state transducers are inadequate for this purpose necessitating the development of neural encoders to serve the growing need of neuromorphic computing. Here, we demonstrate a biomimetic device based on a dual gated MoS2 field effect transistor (FET) capable of encoding analog signals into stochastic spike trains following various neural encoding algorithms such as rate-based encoding, spike timing-based encoding, and spike count-based encoding. Two important aspects of neural encoding, namely, dynamic range and encoding precision are also captured in our demonstration. Furthermore, the encoding energy was found to be as frugal as ≈1-5 pJ/spike. Finally, we show fast (≈200 timesteps) encoding of the MNIST data set using our biomimetic device followed by more than 91% accurate inference using a trained SNN.
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Affiliation(s)
| | - Amritanand Sebastian
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Aaryan Oberoi
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA
| | - Sarbashis Das
- Department of Electrical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Saptarshi Das
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, USA.
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, USA.
- Materials Research Institute, Pennsylvania State University, University Park, PA, USA.
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26
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Frenkel C, Lefebvre M, Bol D. Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks. Front Neurosci 2021; 15:629892. [PMID: 33642986 PMCID: PMC7902857 DOI: 10.3389/fnins.2021.629892] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 01/06/2021] [Indexed: 11/13/2022] Open
Abstract
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of low-cost adaptive smart sensors at the edge, as they severely constrain memory accesses and entail buffering overhead. In this work, we show that the one-hot-encoded labels provided in supervised classification problems, denoted as targets, can be viewed as a proxy for the error sign. Therefore, their fixed random projections enable a layerwise feedforward training of the hidden layers, thus solving the weight transport and update locking problems while relaxing the computational and memory requirements. Based on these observations, we propose the direct random target projection (DRTP) algorithm and demonstrate that it provides a tradeoff between accuracy and computational cost that is suitable for adaptive edge computing devices.
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Affiliation(s)
- Charlotte Frenkel
- Institute of Neuroinformatics, University of Zürich and ETH Zürich, Zurich, Switzerland.,ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Martin Lefebvre
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - David Bol
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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27
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Zhang Y, Qu H, Luo X, Chen Y, Wang Y, Zhang M, Li Z. A new recursive least squares-based learning algorithm for spiking neurons. Neural Netw 2021; 138:110-125. [PMID: 33636484 DOI: 10.1016/j.neunet.2021.01.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 12/15/2020] [Accepted: 01/18/2021] [Indexed: 10/22/2022]
Abstract
Spiking neural networks (SNNs) are regarded as effective models for processing spatio-temporal information. However, their inherent complexity of temporal coding makes it an arduous task to put forward an effective supervised learning algorithm, which still puzzles researchers in this area. In this paper, we propose a Recursive Least Squares-Based Learning Rule (RLSBLR) for SNN to generate the desired spatio-temporal spike train. During the learning process of our method, the weight update is driven by the cost function defined by the difference between the membrane potential and the firing threshold. The amount of weight modification depends not only on the impact of the current error function, but also on the previous error functions which are evaluated by current weights. In order to improve the learning performance, we integrate a modified synaptic delay learning to the proposed RLSBLR. We conduct experiments in different settings, such as spiking lengths, number of inputs, firing rates, noises and learning parameters, to thoroughly investigate the performance of this learning algorithm. The proposed RLSBLR is compared with competitive algorithms of Perceptron-Based Spiking Neuron Learning Rule (PBSNLR) and Remote Supervised Method (ReSuMe). Experimental results demonstrate that the proposed RLSBLR can achieve higher learning accuracy, higher efficiency and better robustness against different types of noise. In addition, we apply the proposed RLSBLR to open source database TIDIGITS, and the results show that our algorithm has a good practical application performance.
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Affiliation(s)
- Yun Zhang
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Hong Qu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
| | - Xiaoling Luo
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Yi Chen
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Yuchen Wang
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Malu Zhang
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
| | - Zefang Li
- China Coal Research Institute, Beijing 100013, PR China
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28
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Knight JC, Nowotny T. Larger GPU-accelerated brain simulations with procedural connectivity. NATURE COMPUTATIONAL SCIENCE 2021; 1:136-142. [PMID: 38217218 DOI: 10.1038/s43588-020-00022-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 12/23/2020] [Indexed: 01/15/2024]
Abstract
Simulations are an important tool for investigating brain function but large models are needed to faithfully reproduce the statistics and dynamics of brain activity. Simulating large spiking neural network models has, until now, needed so much memory for storing synaptic connections that it required high performance computer systems. Here, we present an alternative simulation method we call 'procedural connectivity' where connectivity and synaptic weights are generated 'on the fly' instead of stored and retrieved from memory. This method is particularly well suited for use on graphical processing units (GPUs)-which are a common fixture in many workstations. Using procedural connectivity and an additional GPU code generation optimization, we can simulate a recent model of the macaque visual cortex with 4.13 × 106 neurons and 24.2 × 109 synapses on a single GPU-a significant step forward in making large-scale brain modeling accessible to more researchers.
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Affiliation(s)
- James C Knight
- Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, UK.
| | - Thomas Nowotny
- Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, UK
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29
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Berberian N, Ross M, Chartier S. Embodied working memory during ongoing input streams. PLoS One 2021; 16:e0244822. [PMID: 33400724 PMCID: PMC7785253 DOI: 10.1371/journal.pone.0244822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 12/16/2020] [Indexed: 11/18/2022] Open
Abstract
Sensory stimuli endow animals with the ability to generate an internal representation. This representation can be maintained for a certain duration in the absence of previously elicited inputs. The reliance on an internal representation rather than purely on the basis of external stimuli is a hallmark feature of higher-order functions such as working memory. Patterns of neural activity produced in response to sensory inputs can continue long after the disappearance of previous inputs. Experimental and theoretical studies have largely invested in understanding how animals faithfully maintain sensory representations during ongoing reverberations of neural activity. However, these studies have focused on preassigned protocols of stimulus presentation, leaving out by default the possibility of exploring how the content of working memory interacts with ongoing input streams. Here, we study working memory using a network of spiking neurons with dynamic synapses subject to short-term and long-term synaptic plasticity. The formal model is embodied in a physical robot as a companion approach under which neuronal activity is directly linked to motor output. The artificial agent is used as a methodological tool for studying the formation of working memory capacity. To this end, we devise a keyboard listening framework to delineate the context under which working memory content is (1) refined, (2) overwritten or (3) resisted by ongoing new input streams. Ultimately, this study takes a neurorobotic perspective to resurface the long-standing implication of working memory in flexible cognition.
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Affiliation(s)
- Nareg Berberian
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Matt Ross
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Sylvain Chartier
- Laboratory for Computational Neurodynamics and Cognition, School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
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30
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Rashvand P, Ahmadzadeh MR, Shayegh F. Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition. Int J Neural Syst 2020; 31:2050073. [PMID: 33353527 DOI: 10.1142/s0129065720500732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.
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Affiliation(s)
- Parvaneh Rashvand
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Mohammad Reza Ahmadzadeh
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Farzaneh Shayegh
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
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Yang JQ, Wang R, Ren Y, Mao JY, Wang ZP, Zhou Y, Han ST. Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2003610. [PMID: 33165986 DOI: 10.1002/adma.202003610] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/27/2020] [Indexed: 06/11/2023]
Abstract
The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.
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Affiliation(s)
- Jia-Qin Yang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ruopeng Wang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, P. R. China
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Yi Ren
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Jing-Yu Mao
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Zhan-Peng Wang
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Ye Zhou
- Institute for Advanced Study, Shenzhen University, Shenzhen, 518060, P. R. China
| | - Su-Ting Han
- Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, P. R. China
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32
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Meissner-Bernard C, Tsai MC, Logiaco L, Gerstner W. Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study. Front Synaptic Neurosci 2020; 12:585539. [PMID: 33224033 PMCID: PMC7670913 DOI: 10.3389/fnsyn.2020.585539] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 09/23/2020] [Indexed: 11/21/2022] Open
Abstract
Experiments have shown that the same stimulation pattern that causes Long-Term Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order to understand these, and other, surprising observations we use a phenomenological model of Hebbian plasticity at the location of the synapse. Our model describes the Hebbian condition of joint activity of pre- and postsynaptic neurons in a compact form as the interaction of the glutamate trace left by a presynaptic spike with the time course of the postsynaptic voltage. Instead of simulating the voltage, we test the model using experimentally recorded dendritic voltage traces in hippocampus and neocortex. We find that the time course of the voltage in the neighborhood of a stimulated synapse is a reliable predictor of whether a stimulated synapse undergoes potentiation, depression, or no change. Our computational model can explain the existence of different -at first glance seemingly paradoxical- outcomes of synaptic potentiation and depression experiments depending on the dendritic location of the synapse and the frequency or timing of the stimulation.
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Affiliation(s)
| | | | - Laureline Logiaco
- Center for Theoretical Neuroscience, Columbia University, New York, NY, United States
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33
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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34
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Chen R, Li L. Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation. Neural Comput 2020; 32:2557-2600. [PMID: 32946710 DOI: 10.1162/neco_a_01319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter, we aim to understand the intrinsic limitations of SNN training to design better algorithms. First, the pros and cons of typical SNN training algorithms are analyzed. Then it is found that the spatiotemporal backpropagation algorithm (STBP) has potential in training deep SNNs due to its simplicity and fast convergence. Later, the main bottlenecks of the STBP algorithm are analyzed, and three conditions for training deep SNNs with the STBP algorithm are derived. By analyzing the connection between CNNs and SNNs, we propose a weight initialization algorithm to satisfy the three conditions. Moreover, we propose an error minimization method and a modified loss function to further improve the training performance. Experimental results show that the proposed method achieves 91.53% accuracy on the CIFAR10 data set with 1% accuracy increase over the STBP algorithm and decreases the training epochs on the MNIST data set to 15 epochs (over 13 times speed-up compared to the STBP algorithm). The proposed method also decreases classification latency by over 25 times compared to the CNN-SNN conversion algorithms. In addition, the proposed method works robustly for very deep SNNs, while the STBP algorithm fails in a 19-layer SNN.
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Affiliation(s)
- Ruizhi Chen
- State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190, and University of Chinese Academy of Sciences, Beijing, China 100049
| | - Ling Li
- State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190, and University of Chinese Academy of Sciences, Beijing, China 100049
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35
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Zuo L, Chen Y, Zhang L, Chen C. A spiking neural network with probability information transmission. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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36
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Ceolini E, Frenkel C, Shrestha SB, Taverni G, Khacef L, Payvand M, Donati E. Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing. Front Neurosci 2020; 14:637. [PMID: 32903824 PMCID: PMC7438887 DOI: 10.3389/fnins.2020.00637] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/22/2020] [Indexed: 12/03/2022] Open
Abstract
Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate. Nowadays, with the increasing use of technology, hand-gesture recognition is considered to be an important aspect of Human-Machine Interaction (HMI), allowing the machine to capture and interpret the user's intent and to respond accordingly. The ability to discriminate between human gestures can help in several applications, such as assisted living, healthcare, neuro-rehabilitation, and sports. Recently, multi-sensor data fusion mechanisms have been investigated to improve discrimination accuracy. In this paper, we present a sensor fusion framework that integrates complementary systems: the electromyography (EMG) signal from muscles and visual information. This multi-sensor approach, while improving accuracy and robustness, introduces the disadvantage of high computational cost, which grows exponentially with the number of sensors and the number of measurements. Furthermore, this huge amount of data to process can affect the classification latency which can be crucial in real-case scenarios, such as prosthetic control. Neuromorphic technologies can be deployed to overcome these limitations since they allow real-time processing in parallel at low power consumption. In this paper, we present a fully neuromorphic sensor fusion approach for hand-gesture recognition comprised of an event-based vision sensor and three different neuromorphic processors. In particular, we used the event-based camera, called DVS, and two neuromorphic platforms, Loihi and ODIN + MorphIC. The EMG signals were recorded using traditional electrodes and then converted into spikes to be fed into the chips. We collected a dataset of five gestures from sign language where visual and electromyography signals are synchronized. We compared a fully neuromorphic approach to a baseline implemented using traditional machine learning approaches on a portable GPU system. According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline. These neuromorphic alternatives have increased inference time, between 20 and 40%, with respect to the GPU system but have a significantly smaller energy-delay product (EDP) which makes them between 30× and 600× more efficient. The proposed work represents a new benchmark that moves neuromorphic computing toward a real-world scenario.
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Affiliation(s)
- Enea Ceolini
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Charlotte Frenkel
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Zurich, Switzerland
- ICTEAM Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Sumit Bam Shrestha
- Temasek Laboratories, National University of Singapore, Singapore, Singapore
| | - Gemma Taverni
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Lyes Khacef
- Université Côte d'Azur, CNRS, LEAT, Nice, France
| | - Melika Payvand
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Zurich, Switzerland
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37
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Kaiser J, Mostafa H, Neftci E. Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE). Front Neurosci 2020; 14:424. [PMID: 32477050 PMCID: PMC7235446 DOI: 10.3389/fnins.2020.00424] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/07/2020] [Indexed: 11/29/2022] Open
Abstract
A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge. Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing gradients. DECOLLE is capable of learning deep spatio temporal representations from spikes relying solely on local information, making it compatible with neurobiology and neuromorphic hardware. Synaptic plasticity rules are derived systematically from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. We benchmark our approach on the event-based neuromorphic dataset N-MNIST and DvsGesture, on which DECOLLE performs comparably to the state-of-the-art. DECOLLE networks provide continuously learning machines that are relevant to biology and supportive of event-based, low-power computer vision architectures matching the accuracies of conventional computers on tasks where temporal precision and speed are essential.
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Affiliation(s)
- Jacques Kaiser
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Hesham Mostafa
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Emre Neftci
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
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38
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Lee C, Sarwar SS, Panda P, Srinivasan G, Roy K. Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures. Front Neurosci 2020; 14:119. [PMID: 32180697 PMCID: PMC7059737 DOI: 10.3389/fnins.2020.00119] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 01/30/2020] [Indexed: 12/24/2022] Open
Abstract
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing complex representations while training deep SNNs using input spikes has not been successful so far. Diverse methods have been proposed to get around this issue such as converting off-the-shelf trained deep Artificial Neural Networks (ANNs) to SNNs. However, the ANN-SNN conversion scheme fails to capture the temporal dynamics of a spiking system. On the other hand, it is still a difficult problem to directly train deep SNNs using input spike events due to the discontinuous, non-differentiable nature of the spike generation function. To overcome this problem, we propose an approximate derivative method that accounts for the leaky behavior of LIF neurons. This method enables training deep convolutional SNNs directly (with input spike events) using spike-based backpropagation. Our experiments show the effectiveness of the proposed spike-based learning on deep networks (VGG and Residual architectures) by achieving the best classification accuracies in MNIST, SVHN, and CIFAR-10 datasets compared to other SNNs trained with a spike-based learning. Moreover, we analyze sparse event-based computations to demonstrate the efficacy of the proposed SNN training method for inference operation in the spiking domain.
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39
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Efficient and hardware-friendly methods to implement competitive learning for spiking neural networks. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04755-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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40
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Payvand M, Nair MV, Müller LK, Indiveri G. A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation. Faraday Discuss 2019; 213:487-510. [PMID: 30357205 DOI: 10.1039/c8fd00114f] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility, they are characterized by their computationally relevant physical properties, such as their state-dependence, non-linear conductance changes, and intrinsic variability in both their switching threshold and conductance values, that make them ideal devices for emulating the bio-physics of real synapses. In this paper we present a spiking neural network architecture that supports the use of memristive devices as synaptic elements and propose mixed-signal analog-digital interfacing circuits that mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold for implementing stochastic learning. The effect of device variability is mitigated using pairs of memristive devices configured in a complementary push-pull mechanism and interfaced to a current-mode normalizer circuit. The stochastic learning mechanism is obtained by mapping the desired change in synaptic weight into a corresponding switching probability that is derived from the intrinsic stochastic behavior of memristive devices. We demonstrate the features of the CMOS circuits and apply the architecture proposed to a standard neural network hand-written digit classification benchmark based on the MNIST data-set. We evaluate the performance of the approach proposed in this benchmark using behavioral-level spiking neural network simulation, showing both the effect of the reduction in conductance variability produced by the current-mode normalizer circuit and the increase in performance as a function of the number of memristive devices used in each synapse.
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Affiliation(s)
- Melika Payvand
- Institute of Neuroinformatics, University of Zurich, ETH Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.
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41
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Gastaldi C, Muscinelli S, Gerstner W. Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model. Front Comput Neurosci 2019; 13:78. [PMID: 31798436 PMCID: PMC6874130 DOI: 10.3389/fncom.2019.00078] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 10/21/2019] [Indexed: 01/12/2023] Open
Abstract
Synaptic changes induced by neural activity need to be consolidated to maintain memory over a timescale of hours. In experiments, synaptic consolidation can be induced by repeating a stimulation protocol several times and the effectiveness of consolidation depends crucially on the repetition frequency of the stimulations. We address the question: is there an understandable reason why induction protocols with repetitions at some frequency work better than sustained protocols—even though the accumulated stimulation strength might be exactly the same in both cases? In real synapses, plasticity occurs on multiple time scales from seconds (induction), to several minutes (early phase of long-term potentiation) to hours and days (late phase of synaptic consolidation). We use a simplified mathematical model of just two times scales to elucidate the above question in a purified setting. Our mathematical results show that, even in such a simple model, the repetition frequency of stimulation plays an important role for the successful induction, and stabilization, of potentiation.
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Affiliation(s)
- Chiara Gastaldi
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Samuel Muscinelli
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Wulfram Gerstner
- School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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42
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Frenkel C, Legat JD, Bol D. MorphIC: A 65-nm 738k-Synapse/mm 2 Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:999-1010. [PMID: 31329562 DOI: 10.1109/tbcas.2019.2928793] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient SNNs still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this paper, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86 mm 2 in 65-nm CMOS, achieving a high density of 738k synapses/mm 2. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.
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43
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Deep Spiking Convolutional Neural Network Trained With Unsupervised Spike-Timing-Dependent Plasticity. IEEE Trans Cogn Dev Syst 2019. [DOI: 10.1109/tcds.2018.2833071] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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44
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Yu Q, Li H, Tan KC. Spike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:2178-2189. [PMID: 29993593 DOI: 10.1109/tcyb.2018.2821692] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Spikes play an essential role in information transmission in central nervous system, but how neurons learn from them remains a challenging question. Most algorithms studied how to train spiking neurons to process patterns encoded with a sole assumption of either a rate or a temporal code. Is there a general learning algorithm capable of processing both codes regardless of the intense debate on them within neuroscience community? In this paper, we propose several threshold-driven plasticity algorithms to address the above question. In addition to formulating the algorithms, we also provide proofs with respect to several properties, such as robustness and convergence. The experimental results illustrate that our algorithms are simple, effective and yet efficient for training neurons to learn spike patterns. Due to their simplicity and high efficiency, our algorithms would be potentially beneficial for both software and hardware implementations. Neurons with our algorithms can also detect and recognize embedded features from a background sensory activity. With the as-proposed algorithms, a single neuron can successfully perform multicategory classifications by making decisions based on its output spike number in response to each category. Spike patterns being processed can be encoded with both spike rates and precise timings. When afferent spike timings matter, neurons will automatically extract temporal features without being explicitly instructed as to which point to fire.
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45
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Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F. The Scientific Case for Brain Simulations. Neuron 2019; 102:735-744. [DOI: 10.1016/j.neuron.2019.03.027] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/06/2019] [Accepted: 03/18/2019] [Indexed: 01/30/2023]
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46
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Shi Y, Nguyen L, Oh S, Liu X, Kuzum D. A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications. Front Neurosci 2019; 13:405. [PMID: 31080402 PMCID: PMC6497807 DOI: 10.3389/fnins.2019.00405] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 04/09/2019] [Indexed: 11/13/2022] Open
Abstract
Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs show great potential for the implementation of unsupervised learning using in-memory computing. Here, we report an algorithmic optimization that improves energy efficiency of online learning with SNNs on emerging non-volatile memory (eNVM) devices. We develop a pruning method for SNNs by exploiting the output firing characteristics of neurons. Our pruning method can be applied during network training, which is different from previous approaches in the literature that employ pruning on already-trained networks. This approach prevents unnecessary updates of network parameters during training. This algorithmic optimization can complement the energy efficiency of eNVM technology, which offers a unique in-memory computing platform for the parallelization of neural network operations. Our SNN maintains ~90% classification accuracy on the MNIST dataset with up to ~75% pruning, significantly reducing the number of weight updates. The SNN and pruning scheme developed in this work can pave the way toward applications of eNVM based neuro-inspired systems for energy efficient online learning in low power applications.
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Affiliation(s)
- Yuhan Shi
- Electrical and Computer Engineering Department, University of California, San Diego, San Diego, CA, United States
| | - Leon Nguyen
- Electrical and Computer Engineering Department, University of California, San Diego, San Diego, CA, United States
| | - Sangheon Oh
- Electrical and Computer Engineering Department, University of California, San Diego, San Diego, CA, United States
| | - Xin Liu
- Electrical and Computer Engineering Department, University of California, San Diego, San Diego, CA, United States
| | - Duygu Kuzum
- Electrical and Computer Engineering Department, University of California, San Diego, San Diego, CA, United States
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47
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Pedroni BU, Joshi S, Deiss SR, Sheik S, Detorakis G, Paul S, Augustine C, Neftci EO, Cauwenberghs G. Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity. Front Neurosci 2019; 13:357. [PMID: 31110470 PMCID: PMC6499189 DOI: 10.3389/fnins.2019.00357] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/28/2019] [Indexed: 11/13/2022] Open
Abstract
Spike-Timing-Dependent Plasticity (STDP) is a bio-inspired local incremental weight update rule commonly used for online learning in spike-based neuromorphic systems. In STDP, the intensity of long-term potentiation and depression in synaptic efficacy (weight) between neurons is expressed as a function of the relative timing between pre- and post-synaptic action potentials (spikes), while the polarity of change is dependent on the order (causality) of the spikes. Online STDP weight updates for causal and acausal relative spike times are activated at the onset of post- and pre-synaptic spike events, respectively, implying access to synaptic connectivity both in forward (pre-to-post) and reverse (post-to-pre) directions. Here we study the impact of different arrangements of synaptic connectivity tables on weight storage and STDP updates for large-scale neuromorphic systems. We analyze the memory efficiency for varying degrees of density in synaptic connectivity, ranging from crossbar arrays for full connectivity to pointer-based lookup for sparse connectivity. The study includes comparison of storage and access costs and efficiencies for each memory arrangement, along with a trade-off analysis of the benefits of each data structure depending on application requirements and budget. Finally, we present an alternative formulation of STDP via a delayed causal update mechanism that permits efficient weight access, requiring no more than forward connectivity lookup. We show functional equivalence of the delayed causal updates to the original STDP formulation, with substantial savings in storage and access costs and efficiencies for networks with sparse synaptic connectivity as typically encountered in large-scale models in computational neuroscience.
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Affiliation(s)
- Bruno U Pedroni
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Siddharth Joshi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Stephen R Deiss
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | | | - Georgios Detorakis
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Somnath Paul
- Intel Corporation - Circuit Research Lab, Hillsboro, OR, United States
| | - Charles Augustine
- Intel Corporation - Circuit Research Lab, Hillsboro, OR, United States
| | - Emre O Neftci
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Gert Cauwenberghs
- Integrated Systems Neuroengineering Laboratory, Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Liu D, Yue S. Event-Driven Continuous STDP Learning With Deep Structure for Visual Pattern Recognition. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:1377-1390. [PMID: 29994790 DOI: 10.1109/tcyb.2018.2801476] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Human beings can achieve reliable and fast visual pattern recognition with limited time and learning samples. Underlying this capability, ventral stream plays an important role in object representation and form recognition. Modeling the ventral steam may shed light on further understanding the visual brain in humans and building artificial vision systems for pattern recognition. The current methods to model the mechanism of ventral stream are far from exhibiting fast, continuous, and event-driven learning like the human brain. To create a visual system similar to ventral stream in human with fast learning capability, in this paper, we propose a new spiking neural system with an event-driven continuous spike timing dependent plasticity (STDP) learning method using specific spiking timing sequences. Two novel continuous input mechanisms have been used to obtain the continuous input spiking pattern sequence. With the event-driven STDP learning rule, the proposed learning procedure will be activated if the neuron receive one pre- or post-synaptic spike event. The experimental results on MNIST database show that the proposed method outperforms all other methods in fast learning scenarios and most of the current models in exhaustive learning experiments.
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49
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Nazari S, Faez K. Novel systematic mathematical computation based on the spiking frequency gate (SFG): Innovative organization of spiking computer. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.09.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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50
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Frenkel C, Lefebvre M, Legat JD, Bol D. A 0.086-mm 2 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:145-158. [PMID: 30418919 DOI: 10.1109/tbcas.2018.2880425] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the key requirement of online learning in order to adapt and learn new features in uncontrolled environments. However, embedding online learning in SNNs is currently hindered by high incurred complexity and area overheads. In this paper, we present ODIN, a 0.086-mm 2 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of 12.7 pJ. It leverages an efficient implementation of the spike-driven synaptic plasticity (SDSP) learning rule for high-density embedded online learning with only 0.68 μm 2 per 4-bit synapse. Neurons can be independently configured as a standard leaky integrate-and-fire model or as a custom phenomenological model that emulates the 20 Izhikevich behaviors found in biological spiking neurons. Using a single presentation of 6k 16 × 16 MNIST training images to a single-layer fully-connected 10-neuron network with on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%, while consuming only 15 nJ/inference at 0.55 V using rank order coding. ODIN thus enables further developments toward cognitive neuromorphic devices for low-power, adaptive and low-cost processing.
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