Weighted Population Code for low power neuromorphic image classification

Abstract

Recent digital spiking neuromorphic chips can perform complex computations in real-time with very low power consumption. The input data to such systems needs to first be converted into spikes using a spike encoding scheme. Current examples of such schemes include rate codes and population codes. The selected coding scheme might heavily impact the system's energy consumption, communication bandwidth, processing frame-rate, and computation accuracy. Hence it is important to make an educated decision when selecting the most appropriate spike coding scheme for a given task. To this end, we present a novel spike coding scheme named Weighted Population Code (WPC). WPC is compared to existing coding schemes to transduce images for classification using the TrueNorth chip. Extensive on-chip experimentation with the MNIST and the Flickr-LOGOS32 datasets sheds light on the trade-offs between accuracy, bandwidth, frame rate, network size and energy consumption for image classification, showing the advantages of WPC when high dynamic range and accuracy are needed.