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 exploited well. 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 20.9x, 27.9x, 88.4x, and 1586x, respectively, compared to the conventional scheme, which raises the possibility of implementing large-scale CNN on neuromorphic hardware.