A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware.

Chenglong Zou,Yuan Wang,Yisong Kuang,Xinan Wang,Xiaoxin Cui,Kefei Liu,Ru Huang

doi:10.3389/fnins.2021.694170

Chenglong Zou, Yuan Wang + Show 5 more

Open Access

https://doi.org/10.3389/fnins.2021.694170

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Abstract

Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called “scatter-and-gather” to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {−1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.

Highlights

Deep convolutional neural network (CNN) architectures such as VGG-Net (Simonyan and Zisserman, 2014) and ResNet (He et al, 2016) have achieved close to, even beyond human-level performance in many computer vision tasks such as image classification (Russakovsky et al, 2015) and object detection (Lin et al, 2014) in recent years
This work aims to overcome the aforementioned drawbacks in Artificial neural networks (ANNs)-to-spiking neural networks (SNNs) conversion process and hardware implementation, i.e., to present a more accurate, general, and hardware-friendly conversion method, which is compatible with contemporary neuromorphic hardware
This work is based on our previous algorithm (Zou et al, 2020) and hardware (Kuang et al, 2021), and we extend it by (a) testing its robustness on input noise and larger dataset (CIFAR-100), (b) developing a incremental mapping framework to carry out an efficient network deployment on a typical crossbar-based neuromorphic chip, (c) detailed power and speed analyses are given to show its excellent application potential

Summary

Introduction

Deep convolutional neural network (CNN) architectures such as VGG-Net (Simonyan and Zisserman, 2014) and ResNet (He et al, 2016) have achieved close to, even beyond human-level performance in many computer vision tasks such as image classification (Russakovsky et al, 2015) and object detection (Lin et al, 2014) in recent years. Lots of works presented various compression (Deng et al, 2020) and quantization methods (Hubara et al, 2016) of neural network or concentrated on less memory access and pipeline optimizing in custom CNN accelerators (Lecun, 2019; Chen et al, 2020), which greatly improved computation efficiency and reduced power consumption. Considering another kind of emerging approach to incorporate biological plausibility of brain-inspired models and efficient neuromorphic hardware primitives, spiking neural networks (SNNs) (Grning and Bohte, 2014) attract more attention. This discrete spiking dynamic behavior is quite different from ANNs, in which the activation function is continuous

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