A Fast and Energy-Efficient SNN Processor With Adaptive Clock/Event-Driven Computation Scheme and Online Learning

Li, Sixu; Zhang, Zhaomin; Mao, Ruixin; Xiao, Jianbiao; Chang, Liang; Zhou, Jun

doi:10.1109/tcsi.2021.3052885

Cited by 87 publications

(28 citation statements)

References 24 publications

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“…Spiking neural networks (SNNs) (Maass, 1997 ) have attracted increasing attention because of their characteristics, including preferable biological interpretability and low-power processing potential (Akopyan et al, 2015 ; Shen et al, 2016 ; Davies et al, 2018 ; Moradi et al, 2018 ; Pei et al, 2019 ; Li et al, 2021 ; Pham et al, 2021 ). Compared to traditional artificial neural networks (ANNs), SNNs increase the time dimension so that they naturally support information processing in the temporal domain.…”

Section: Introductionmentioning

confidence: 99%

EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering

Tao²

2022

Front. Neurosci.

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Recently, spiking neural networks (SNNs) have been widely studied by researchers due to their biological interpretability and potential application of low power consumption. However, the traditional clock-driven simulators have the problem that the accuracy is limited by the time-step and the lateral inhibition failure. To address this issue, we introduce EvtSNN (Event SNN), a faster SNN event-driven simulator inspired by EDHA (Event-Driven High Accuracy). Two innovations are proposed to accelerate the calculation of event-driven neurons. Firstly, the intermediate results can be reused in population computing without repeated calculations. Secondly, unnecessary peak calculations will be skipped according to a condition. In the MNIST classification task, EvtSNN took 56 s to complete one epoch of unsupervised training and achieved 89.56% accuracy, while EDHA takes 642 s. In the benchmark experiments, the simulation speed of EvtSNN is 2.9–14.0 times that of EDHA under different network scales.

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Section: Introductionmentioning

confidence: 99%

EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering

Tao²

2022

Front. Neurosci.

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“…Limited resources induce a latency-accuracy trade-off at individual agents (also attention vs. precision in robotics): sensors can either send raw, inaccurate measurements to the base station, or refine them locally before transmission, incurring extra processing delay due to hardware-constrained computation. Common options are averaging or filtering of noisy samples, compression of images or other high-dimensional data [18], [19], or descent direction computation in online learning [20], [21]. The dynamical nature of the monitored system makes such delayed processed measurements obsolete, so that sensing design for multiple, possibly heterogeneous, agents becomes nontrivial at network level, and may require online adaptation of local processing.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

Ballotta¹,

Peserico²,

Zanini³

2022

Preprint

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In this paper, we consider a wireless network of smart sensors (agents) that monitor a dynamical process and send measurements to a base station that performs global monitoring and decision-making. Smart sensors are equipped with both sensing and computation, and can either send raw measurements or process them prior to transmission. Constrained agent resources raise a fundamental latency-accuracy trade-off. On the one hand, raw measurements are inaccurate but fast to produce. On the other hand, data processing on resource-constrained platforms generates accurate measurements at the cost of non-negligible computation latency. Further, if processed data are also compressed, latency caused by wireless communication might be higher for raw measurements. Hence, it is challenging to decide when and where sensors in the network should transmit raw measurements or leverage time-consuming local processing. To tackle this design problem, we propose a Reinforcement Learning approach to learn an efficient policy that dynamically decides when measurements are to be processed at each sensor. Effectiveness of our proposed approach is validated through a numerical simulation with case study on smart sensing motivated by the Internet of Drones. This work has been partially funded by the Italian Ministry of Education, University and Research (MIUR) through the PRIN project no. 2017NS9FEY entitled "Realtime Control of 5G Wireless Networks: Taming the Complexity of Future Transmission and Computation Challenges" and through the initiative "Departments of Excellence" (Law 232/2016). The views and opinions expressed in this work are those of the authors and do not necessarily reflect those of the funding institutions.

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“…To fully exploit the computational and energy efficiency of SNNs, various dedicate neuromorphic chips and hardware systems have recently been designed [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. These VLSI chips support various spiking neuron models at different levels of biological fidelity and computational complexity, and generally adopt scalable routing schemes including crossbars and network-on-chip (NoC) infrastructures towards large-scale or even brain-scale multichip systems.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, general-purpose computers such as the Central Processing Unit (CPU) and the Graphics Processing Unit (GPU) are incompetent in deploying brain-inspired SNN models, as those von Neumann machines are oriented for dense numerical calculations rather than sparse temporal spike processing. To fully exploit the computational and energy efficiency of SNNs, various dedicate neuromorphic chips and hardware systems have recently been designed [2][3][4][5][6][7][8][9][10][11][12][13][14]. These VLSI chips support various spiking neuron models at different levels of biological fidelity and computational complexity, and generally adopt scalable routing schemes including crossbars and network-on-chip (NoC) infrastructures towards large-scale or even brain-scale multichip systems.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps

Zhang

Yang

Shi

et al. 2021

Sensors

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Neuromorphic hardware systems have been gaining ever-increasing focus in many embedded applications as they use a brain-inspired, energy-efficient spiking neural network (SNN) model that closely mimics the human cortex mechanism by communicating and processing sensory information via spatiotemporally sparse spikes. In this paper, we fully leverage the characteristics of spiking convolution neural network (SCNN), and propose a scalable, cost-efficient, and high-speed VLSI architecture to accelerate deep SCNN inference for real-time low-cost embedded scenarios. We leverage the snapshot of binary spike maps at each time-step, to decompose the SCNN operations into a series of regular and simple time-step CNN-like processing to reduce hardware resource consumption. Moreover, our hardware architecture achieves high throughput by employing a pixel stream processing mechanism and fine-grained data pipelines. Our Zynq-7045 FPGA prototype reached a high processing speed of 1250 frames/s and high recognition accuracies on the MNIST and Fashion-MNIST image datasets, demonstrating the plausibility of our SCNN hardware architecture for many embedded applications.

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A Fast and Energy-Efficient SNN Processor With Adaptive Clock/Event-Driven Computation Scheme and Online Learning

Cited by 87 publications

References 24 publications

EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering

EvtSNN: Event-driven SNN simulator optimized by population and pre-filtering

A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems

A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps

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