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

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.

Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning

“…A research is booming in using LIF spiking networks for online learning 27 , braille letter reading 28 , different neuromorphic synaptic devices 29 for detection and classification of biological problems [30][31][32][33][34][35][36] . Significant research is focused on making human-level control 37 , optimizing back-propagation algorithms for spiking networks [38][39][40] , as well as penetrating much deeper into ARCSes core [41][42][43][44] with smaller number of time steps 41 , using an event-driven paradigm 36,40,45,46 , applying batch normalization 47 , scatter-and-gather optimizations 48 , supervised plasticity 49 , time-step binary maps 50 , and using transfer learning algorithms 51 . In concert with this broad range of software applications, there is a huge amount of research directed at developing and using these LIF SNN in embedded applications with the help of the neuromorphic hardware [52][53][54][55][56][57] , the generic name given to hardware that is nominally based on, or inspired by, the structure and function of the human brain.…”