Compact Convolution Mapping on Neuromorphic Hardware using Axonal Delay

Abstract

Mapping Convolutional Neural Network (CNN) to a neuromorphic hardware has been inefficient in synapse memory usage because both kernel/input reuse are not exploitedwell.We propose a method to enable kernel reuse by utilizing axonal delay, which is a biological parameter for a spiking neuron. Using IBM TrueNorth as a test platform, we demonstrate that the number of cores, neurons, synapses, and synaptic operations per time step can be reduced by up to 20.9×, 27.9×, 88.4×, and 1586×, respectively, compared to the conventional scheme, which raises the possibility of implementing large-scale CNN on neuromorphic hardware.