Weighted Population Code For Low Power Neuromorphic Image Classification

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 framerate, 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.