1
|
Zhang W, Geng H, Li P. Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization. Front Neurosci 2024; 18:1412559. [PMID: 38966757 PMCID: PMC11222634 DOI: 10.3389/fnins.2024.1412559] [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: 04/05/2024] [Accepted: 06/03/2024] [Indexed: 07/06/2024] Open
Abstract
In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired "self-repairing" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural "evolution." We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.
Collapse
Affiliation(s)
| | | | - Peng Li
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
| |
Collapse
|
2
|
Wang Z, Tao P, Chen L. Brain-inspired chaotic spiking backpropagation. Natl Sci Rev 2024; 11:nwae037. [PMID: 38707198 PMCID: PMC11067972 DOI: 10.1093/nsr/nwae037] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/19/2023] [Accepted: 01/17/2024] [Indexed: 05/07/2024] Open
Abstract
Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.
Collapse
Affiliation(s)
- Zijian Wang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Peng Tao
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Luonan Chen
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China
- Pazhou Laboratory (Huangpu), Guangzhou 510555, China
| |
Collapse
|
3
|
Yao M, Richter O, Zhao G, Qiao N, Xing Y, Wang D, Hu T, Fang W, Demirci T, De Marchi M, Deng L, Yan T, Nielsen C, Sheik S, Wu C, Tian Y, Xu B, Li G. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat Commun 2024; 15:4464. [PMID: 38796464 PMCID: PMC11127998 DOI: 10.1038/s41467-024-47811-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 04/12/2024] [Indexed: 05/28/2024] Open
Abstract
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called "Speck", a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the "dynamic imbalance" in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature.
Collapse
Affiliation(s)
- Man Yao
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ole Richter
- SynSense AG Corporation, Zurich, Switzerland
| | - Guangshe Zhao
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Ning Qiao
- SynSense AG Corporation, Zurich, Switzerland
- SynSense Corporation, Chengdu, Sichuan, China
| | - Yannan Xing
- SynSense Corporation, Chengdu, Sichuan, China
| | - Dingheng Wang
- Northwest Institute of Mechanical & Electrical Engineering, Xianyang, Shaanxi, China
| | - Tianxiang Hu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wei Fang
- School of Computer Science, Peking University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | | | | | - Lei Deng
- Center for Brain-Inspired Computing, Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Carsten Nielsen
- SynSense AG Corporation, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Chenxi Wu
- SynSense AG Corporation, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Yonghong Tian
- School of Computer Science, Peking University, Beijing, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Beijing, China.
| |
Collapse
|
4
|
Wei X, Cheng S, Chen R, Wang Z, Li Y. ANN deformation prediction model for deep foundation pit with considering the influence of rainfall. Sci Rep 2023; 13:22664. [PMID: 38114655 PMCID: PMC10730717 DOI: 10.1038/s41598-023-49579-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/09/2023] [Indexed: 12/21/2023] Open
Abstract
Deep foundation pits involving complex soil-water-structure interactions are often at a high risk of failure under heavy rainfall. Predicted deformation is an important index for early risk warning. In the study, an ANN model is proposed based on the Wave Transform (WT), Copula method, Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The total deformation was firstly decomposed into low and high frequency components with WT. The CNN and LSTM were then used for prediction of the two components with rolling training and prediction. The input variables of the CNN and LSTM were determined and optimized based on the correlations analysis of Copula method of the two components with different random variables, especially with the rainfall. And finally, the predicted total deformation was obtained by adding the two prediction components. A deep foundation pit in Chengdu, China was taken as a case study, of which the horizontal deformation curves at different measuring points shows three types of developed trend, as unstable, less stable, and stable types. The predictions of the deformations of different development types by the proposed ANN model show high accuracies with a few input variables and can accurately prompt risk warning in advance.
Collapse
Affiliation(s)
- Xing Wei
- Department of Geotechnical Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
| | - Shitao Cheng
- Sichuan Vocational and Technical College of Communications, Chengdu, 611130, China
| | - Rui Chen
- Department of Geotechnical Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Zijian Wang
- Sichuan Vocational and Technical College of Communications, Chengdu, 611130, China
| | - Yanjun Li
- Department of Geotechnical Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| |
Collapse
|
5
|
Pei Y, Xu C, Wu Z, Liu Y, Yang Y. ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator. Front Neurosci 2023; 17:1225871. [PMID: 37771337 PMCID: PMC10525310 DOI: 10.3389/fnins.2023.1225871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Spiking neural network (SNN) is a brain-inspired model with more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. In this study, we propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators, which dynamically selects the network layers to be binarized to ensure a balance between quantization degree and classification accuracy by evaluating the error caused by the binarized weights during the network learning process. At the same time, to accelerate the training speed of the network, the global average pooling (GAP) layer is introduced to replace the fully connected layers by combining convolution and pooling. Finally, to further reduce the error caused by the binary weight, we propose binary weight optimization (BWO), which updates the overall weight by directly adjusting the binary weight. This method further reduces the loss of the network that reaches the training bottleneck. The combination of the above methods balances the network's quantization and recognition ability, enabling the network to maintain the recognition capability equivalent to the full precision network and reduce the storage space by more than 20%. So, SNNs can use a small number of time steps to obtain better recognition accuracy. In the extreme case of using only a one-time step, we still can achieve 93.39, 92.12, and 69.55% testing accuracy on three traditional static datasets, Fashion- MNIST, CIFAR-10, and CIFAR-100, respectively. At the same time, we evaluate our method on neuromorphic N-MNIST, CIFAR10-DVS, and IBM DVS128 Gesture datasets and achieve advanced accuracy in SNN with binary weights. Our network has greater advantages in terms of storage resources and training time.
Collapse
Affiliation(s)
- Yijian Pei
- Guangzhou Institute of Technology, Xidian University, Xi'an, China
| | - Changqing Xu
- Guangzhou Institute of Technology, Xidian University, Xi'an, China
- School of Microelectronics, Xidian University, Xi'an, China
| | - Zili Wu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yi Liu
- School of Microelectronics, Xidian University, Xi'an, China
| | - Yintang Yang
- School of Microelectronics, Xidian University, Xi'an, China
| |
Collapse
|
6
|
Yao M, Zhang H, Zhao G, Zhang X, Wang D, Cao G, Li G. Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition. Neural Netw 2023; 166:410-423. [PMID: 37549609 DOI: 10.1016/j.neunet.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/23/2023] [Accepted: 07/05/2023] [Indexed: 08/09/2023]
Abstract
Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally suitable for dealing with event streams due to its temporal information processing capability and event-driven nature. However, existing works SNN neglect the fact that the input event streams are spatially sparse and temporally non-uniform, and just treat these variant inputs equally. This situation interferes with the effectiveness and efficiency of existing SNNs. In this paper, we propose the feature Refine-and-Mask SNN (RM-SNN), which has the ability of self-adaption to regulate the spiking response in a data-dependent way. We use the Refine-and-Mask (RM) module to refine all features and mask the unimportant features to optimize the membrane potential of spiking neurons, which in turn drops the spiking activity. Inspired by the fact that not all events in spatio-temporal streams are task-relevant, we execute the RM module in both temporal and channel dimensions. Extensive experiments on seven event-based benchmarks, DVS128 Gesture, DVS128 Gait, CIFAR10-DVS, N-Caltech101, DailyAction-DVS, UCF101-DVS, and HMDB51-DVS demonstrate that under the multi-scale constraints of input time window, RM-SNN can significantly reduce the network average spiking activity rate while improving the task performance. In addition, by visualizing spiking responses, we analyze why sparser spiking activity can be better. Code.
Collapse
Affiliation(s)
- Man Yao
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Peng Cheng Laboratory, Shenzhen 518000, China.
| | - Hengyu Zhang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China.
| | - Guangshe Zhao
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Xiyu Zhang
- School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
| | - Dingheng Wang
- Northwest Institute of Mechanical & Electrical Engineering, Xianyang, Shaanxi, China.
| | - Gang Cao
- Beijing Academy of Artificial Intelligence, Beijing 100089, China
| | - Guoqi Li
- Peng Cheng Laboratory, Shenzhen 518000, China; Institute of Automation, Chinese Academy of Sciences, Beijing 100089, China.
| |
Collapse
|
7
|
Aboumerhi K, Güemes A, Liu H, Tenore F, Etienne-Cummings R. Neuromorphic applications in medicine. J Neural Eng 2023; 20:041004. [PMID: 37531951 DOI: 10.1088/1741-2552/aceca3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/02/2023] [Indexed: 08/04/2023]
Abstract
In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.
Collapse
Affiliation(s)
- Khaled Aboumerhi
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Amparo Güemes
- Electrical Engineering Division, Department of Engineering, University of Cambridge, 9 JJ Thomson Ave, Cambridge CB3 0FA, United Kingdom
| | - Hongtao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Francesco Tenore
- Research and Exploratory Development Department, The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States of America
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States of America
| |
Collapse
|
8
|
Wang D, Wu B, Zhao G, Yao M, Chen H, Deng L, Yan T, Li G. Kronecker CP Decomposition With Fast Multiplication for Compressing RNNs. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:2205-2219. [PMID: 34534089 DOI: 10.1109/tnnls.2021.3105961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition. However, because the modern RNNs have complex topologies and expensive space/computation complexity, compressing them becomes a hot and promising topic in recent years. Among plenty of compression methods, tensor decomposition, e.g., tensor train (TT), block term (BT), tensor ring (TR), and hierarchical Tucker (HT), appears to be the most amazing approach because a very high compression ratio might be obtained. Nevertheless, none of these tensor decomposition formats can provide both space and computation efficiency. In this article, we consider to compress RNNs based on a novel Kronecker CANDECOMP/PARAFAC (KCP) decomposition, which is derived from Kronecker tensor (KT) decomposition, by proposing two fast algorithms of multiplication between the input and the tensor-decomposed weight. According to our experiments based on UCF11, Youtube Celebrities Face, UCF50, TIMIT, TED-LIUM, and Spiking Heidelberg digits datasets, it can be verified that the proposed KCP-RNNs have a comparable performance of accuracy with those in other tensor-decomposed formats, and even 278 219× compression ratio could be obtained by the low-rank KCP. More importantly, KCP-RNNs are efficient in both space and computation complexity compared with other tensor-decomposed ones. Besides, we find KCP has the best potential of parallel computing to accelerate the calculations in neural networks.
Collapse
|
9
|
Winston CN, Mastrovito D, Shea-Brown E, Mihalas S. Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks. Neural Comput 2023; 35:555-592. [PMID: 36827598 PMCID: PMC10044000 DOI: 10.1162/neco_a_01571] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 11/02/2022] [Indexed: 02/26/2023]
Abstract
Individual neurons in the brain have complex intrinsic dynamics that are highly diverse. We hypothesize that the complex dynamics produced by networks of complex and heterogeneous neurons may contribute to the brain's ability to process and respond to temporally complex data. To study the role of complex and heterogeneous neuronal dynamics in network computation, we develop a rate-based neuronal model, the generalized-leaky-integrate-and-fire-rate (GLIFR) model, which is a rate equivalent of the generalized-leaky-integrate-and-fire model. The GLIFR model has multiple dynamical mechanisms, which add to the complexity of its activity while maintaining differentiability. We focus on the role of after-spike currents, currents induced or modulated by neuronal spikes, in producing rich temporal dynamics. We use machine learning techniques to learn both synaptic weights and parameters underlying intrinsic dynamics to solve temporal tasks. The GLIFR model allows the use of standard gradient descent techniques rather than surrogate gradient descent, which has been used in spiking neural networks. After establishing the ability to optimize parameters using gradient descent in single neurons, we ask how networks of GLIFR neurons learn and perform on temporally challenging tasks, such as sequential MNIST. We find that these networks learn diverse parameters, which gives rise to diversity in neuronal dynamics, as demonstrated by clustering of neuronal parameters. GLIFR networks have mixed performance when compared to vanilla recurrent neural networks, with higher performance in pixel-by-pixel MNIST but lower in line-by-line MNIST. However, they appear to be more robust to random silencing. We find that the ability to learn heterogeneity and the presence of after-spike currents contribute to these gains in performance. Our work demonstrates both the computational robustness of neuronal complexity and diversity in networks and a feasible method of training such models using exact gradients.
Collapse
Affiliation(s)
- Chloe N Winston
- Departments of Neuroscience and Computer Science, University of Washington, Seattle, WA 98195, U.S.A
- University of Washington Computational Neuroscience Center, Seattle, WA 98195, U.S.A.
| | - Dana Mastrovito
- Allen Institute for Brain Science, Seattle, WA 98109, U.S.A.
| | - Eric Shea-Brown
- University of Washington Computational Neuroscience Center, Seattle, WA 98195, U.S.A
- Allen Institute for Brain Science, Seattle, WA 98109, U.S.A
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A.
| | - Stefan Mihalas
- University of Washington Computational Neuroscience Center, Seattle, WA 98195, U.S.A
- Allen Institute for Brain Science, Seattle, WA 98109, U.S.A
- Department of Applied Mathematics, University of Washington, Seattle, WA 98195, U.S.A.
| |
Collapse
|
10
|
Yi Z, Lian J, Liu Q, Zhu H, Liang D, Liu J. Learning Rules in Spiking Neural Networks: A Survey. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.02.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
|
11
|
Guo W, Fouda ME, Eltawil AM, Salama KN. Efficient training of spiking neural networks with temporally-truncated local backpropagation through time. Front Neurosci 2023; 17:1047008. [PMID: 37090791 PMCID: PMC10117667 DOI: 10.3389/fnins.2023.1047008] [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: 09/17/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.
Collapse
Affiliation(s)
- Wenzhe Guo
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Communication and Computing Systems Lab, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Mohammed E. Fouda
- Center for Embedded & Cyber-Physical Systems, University of California, Irvine, Irvine, CA, United States
| | - Ahmed M. Eltawil
- Communication and Computing Systems Lab, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Center for Embedded & Cyber-Physical Systems, University of California, Irvine, Irvine, CA, United States
| | - Khaled Nabil Salama
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- *Correspondence: Khaled Nabil Salama
| |
Collapse
|
12
|
Müller-Cleve SF, Fra V, Khacef L, Pequeño-Zurro A, Klepatsch D, Forno E, Ivanovich DG, Rastogi S, Urgese G, Zenke F, Bartolozzi C. Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Front Neurosci 2022; 16:951164. [PMID: 36440280 PMCID: PMC9695069 DOI: 10.3389/fnins.2022.951164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/19/2022] [Indexed: 03/25/2024] Open
Abstract
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities. Recent deep learning approaches have reached outstanding accuracies in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through Braille letter reading. We recorded a new Braille letters dataset based on the capacitive tactile sensors of the iCub robot's fingertip. We then investigated the importance of spatial and temporal information as well as the impact of event-based encoding on spike-based computation. Afterward, we trained and compared feedforward and recurrent Spiking Neural Networks (SNNs) offline using Backpropagation Through Time (BPTT) with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We compared our approach to standard classifiers, in particular to the Long Short-Term Memory (LSTM) deployed on the embedded NVIDIA Jetson GPU, in terms of classification accuracy, power, and energy consumption together with computational delay. Our results show that the LSTM reaches ~97% of accuracy, outperforming the recurrent SNN by ~17% when using continuous frame-based data instead of event-based inputs. However, the recurrent SNN on Loihi with event-based inputs is ~500 times more energy-efficient than the LSTM on Jetson, requiring a total power of only ~30 mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware, and spike-based computing for spatio-temporal pattern recognition at the edge.
Collapse
Affiliation(s)
| | - Vittorio Fra
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Lyes Khacef
- Bio-Inspired Circuits and Systems Lab, Zernike Institute for Advanced Materials, Groningen Cognitive Systems and Materials Center, University of Groningen, Groningen, Netherlands
| | | | - Daniel Klepatsch
- Silicon Austria Labs, Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Graz, Austria
- Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Johannes Kepler University Linz, Graz, Austria
| | - Evelina Forno
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Diego G. Ivanovich
- Silicon Austria Labs, Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Graz, Austria
- Johannes Kepler Universität (JKU) Linz Institute of Technology (LIT) Silicon Austria Labs (SAL) embedded Signal Processing and Machine Learning (eSPML) Lab, Johannes Kepler University Linz, Graz, Austria
| | - Shavika Rastogi
- International Centre for Neuromorphic Systems, Western Sydney University, Penrith, NSW, Australia
- Biocomputation Research Group, University of Hertfordshire, Hatfield, United Kingdom
| | - Gianvito Urgese
- Politecnico di Torino, Electronic Design Automation (EDA) Group, Torino, Italy
| | - Friedemann Zenke
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Chiara Bartolozzi
- Istituto Italiano di Tecnologia, Event-Driven Perception in Robotics, Genoa, Italy
| |
Collapse
|
13
|
Zhang S, Wang W, Li H, Zhang S. EVtracker: An Event-Driven Spatiotemporal Method for Dynamic Object Tracking. SENSORS (BASEL, SWITZERLAND) 2022; 22:6090. [PMID: 36015851 PMCID: PMC9414578 DOI: 10.3390/s22166090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/06/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
An event camera is a novel bio-inspired sensor that effectively compensates for the shortcomings of current frame cameras, which include high latency, low dynamic range, motion blur, etc. Rather than capturing images at a fixed frame rate, an event camera produces an asynchronous signal by measuring the brightness change of each pixel. Consequently, an appropriate algorithm framework that can handle the unique data types of event-based vision is required. In this paper, we propose a dynamic object tracking framework using an event camera to achieve long-term stable tracking of event objects. One of the key novel features of our approach is to adopt an adaptive strategy that adjusts the spatiotemporal domain of event data. To achieve this, we reconstruct event images from high-speed asynchronous streaming data via online learning. Additionally, we apply the Siamese network to extract features from event data. In contrast to earlier models that only extract hand-crafted features, our method provides powerful feature description and a more flexible reconstruction strategy for event data. We assess our algorithm in three challenging scenarios: 6-DoF (six degrees of freedom), translation, and rotation. Unlike fixed cameras in traditional object tracking tasks, all three tracking scenarios involve the simultaneous violent rotation and shaking of both the camera and objects. Results from extensive experiments suggest that our proposed approach achieves superior accuracy and robustness compared to other state-of-the-art methods. Without reducing time efficiency, our novel method exhibits a 30% increase in accuracy over other recent models. Furthermore, results indicate that event cameras are capable of robust object tracking, which is a task that conventional cameras cannot adequately perform, especially for super-fast motion tracking and challenging lighting situations.
Collapse
|
14
|
Relaxation LIF: A gradient-based spiking neuron for direct training deep spiking neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
15
|
Dong J, Jiang R, Xiao R, Yan R, Tang H. Event stream learning using spatio-temporal event surface. Neural Netw 2022; 154:543-559. [DOI: 10.1016/j.neunet.2022.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/12/2022] [Accepted: 07/10/2022] [Indexed: 11/29/2022]
|
16
|
Spiking Neural Networks and Their Applications: A Review. Brain Sci 2022; 12:brainsci12070863. [PMID: 35884670 PMCID: PMC9313413 DOI: 10.3390/brainsci12070863] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/12/2022] [Accepted: 06/13/2022] [Indexed: 02/04/2023] Open
Abstract
The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.
Collapse
|
17
|
Lian S, Liu Q, Yan R, Pan G, Tang H. Training Deep Convolutional Spiking Neural Networks With Spike Probabilistic Global Pooling. Neural Comput 2022; 34:1170-1188. [PMID: 35231931 DOI: 10.1162/neco_a_01480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/14/2021] [Indexed: 11/04/2022]
Abstract
Recent work on spiking neural networks (SNNs) has focused on achieving deep architectures. They commonly use backpropagation (BP) to train SNNs directly, which allows SNNs to go deeper and achieve higher performance. However, the BP training procedure is computing intensive and complicated by many trainable parameters. Inspired by global pooling in convolutional neural networks (CNNs), we present the spike probabilistic global pooling (SPGP) method based on a probability function for training deep convolutional SNNs. It aims to remove the difficult of too many trainable parameters brought by multiple layers in the training process, which can reduce the risk of overfitting and get better performance for deep SNNs (DSNNs). We use the discrete leaky-integrate-fire model and the spatiotemporal BP algorithm for training DSNNs directly. As a result, our model trained with the SPGP method achieves competitive performance compared to the existing DSNNs on image and neuromorphic data sets while minimizing the number of trainable parameters. In addition, the proposed SPGP method shows its effectiveness in performance improvement, convergence, and generalization ability.
Collapse
Affiliation(s)
- Shuang Lian
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Qianhui Liu
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Rui Yan
- College of Computer Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.,Zhejiang Lab, Hangzhou 311121, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.,Zhejiang Lab, Hangzhou 311121, China
| |
Collapse
|
18
|
An accurate and fair evaluation methodology for SNN-based inferencing with full-stack hardware design space explorations. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
19
|
Ghimire S, Yaseen ZM, Farooque AA, Deo RC, Zhang J, Tao X. Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Sci Rep 2021; 11:17497. [PMID: 34471166 PMCID: PMC8410863 DOI: 10.1038/s41598-021-96751-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 08/13/2021] [Indexed: 11/09/2022] Open
Abstract
Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-Weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s-1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction.
Collapse
Affiliation(s)
- Sujan Ghimire
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Zaher Mundher Yaseen
- New era and development in civil engineering research group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq.
- College of Creative Design, Asia University, Taichung City, Taiwan.
| | - Aitazaz A Farooque
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, C1A4P3, Canada
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Ji Zhang
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| | - Xiaohui Tao
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
| |
Collapse
|
20
|
Iyer LR, Chua Y, Li H. Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain. Front Neurosci 2021; 15:608567. [PMID: 33841072 PMCID: PMC8027306 DOI: 10.3389/fnins.2021.608567] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 03/01/2021] [Indexed: 11/26/2022] Open
Abstract
A major characteristic of spiking neural networks (SNNs) over conventional artificial neural networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and efficient computing. In this paper, we assess if neuromorphic datasets recorded from static images are able to evaluate the ability of SNNs to use spike timings in their calculations. We have analyzed N-MNIST, N-Caltech101 and DvsGesture along these lines, but focus our study on N-MNIST. First we evaluate if additional information is encoded in the time domain in a neuromorphic dataset. We show that an ANN trained with backpropagation on frame-based versions of N-MNIST and N-Caltech101 images achieve 99.23 and 78.01% accuracy. These are comparable to the state of the art-showing that an algorithm that purely works on spatial data can classify these datasets. Second we compare N-MNIST and DvsGesture on two STDP algorithms, RD-STDP, that can classify only spatial data, and STDP-tempotron that classifies spatiotemporal data. We demonstrate that RD-STDP performs very well on N-MNIST, while STDP-tempotron performs better on DvsGesture. Since DvsGesture has a temporal dimension, it requires STDP-tempotron, while N-MNIST can be adequately classified by an algorithm that works on spatial data alone. This shows that precise spike timings are not important in N-MNIST. N-MNIST does not, therefore, highlight the ability of SNNs to classify temporal data. The conclusions of this paper open the question-what dataset can evaluate SNN ability to classify temporal data?
Collapse
Affiliation(s)
- Laxmi R. Iyer
- Neuromorphic Computing, Institute of Infocomms Research, A*Star, Singapore, Singapore
| | - Yansong Chua
- Neuromorphic Computing, Institute of Infocomms Research, A*Star, Singapore, Singapore
| | - Haizhou Li
- Neuromorphic Computing, Institute of Infocomms Research, A*Star, Singapore, Singapore
- Huawei Technologies Co., Ltd., Shenzhen, China
| |
Collapse
|
21
|
Deng L, Tang H, Roy K. Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning. Front Comput Neurosci 2021; 15:665662. [PMID: 33815083 PMCID: PMC8010134 DOI: 10.3389/fncom.2021.665662] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 11/19/2022] Open
Affiliation(s)
- Lei Deng
- Department of Precision Instrument, Center for Brain Inspired Computing Research (CBICR), Tsinghua University, Beijing, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- Zhejiang Lab, Hangzhou, China
| | - Kaushik Roy
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| |
Collapse
|