Low Latency Conversion of Artificial Neural Network Models to Rate-Encoded Spiking Neural Networks.

Abstract

Spiking neural networks (SNNs) are well suited for resource-constrained applications as they do not need expensive multipliers. In a typical rate-encoded SNN, a series of binary spikes within a globally fixed time window is used to fire the neurons. The time window size is also the latency of the network in performing a single inference, as well as determining the overall energy efficiency of the model. The aim of this article is to reduce this while maintaining accuracy when converting artificial neural networks (ANNs) to their equivalent SNNs. The state-of-the-art conversion schemes yield SNNs with accuracies comparable with ANNs only for large window sizes. In this article, we start with understanding the information loss when converting from preexisting ANN models to standard rate-encoded SNN models. From these insights, we propose a suite of techniques that includes a novel SNN encoding scheme, a new spike generation model, an input channel expansion strategy, and a threshold training technique. Together, these methods enabled us to achieve state-of-the-art accuracies using the lowest latencies reported in the literature. In particular, our method achieved a top-1 SNN accuracy of 98.73% (using a single time step) on the MNIST dataset, 76.38% (with eight time steps) on the CIFAR-100 dataset, and 93.71% (eight time steps) on the CIFAR-10 dataset. On ImageNet, an SNN accuracy of 81.9% was achieved using 40 time steps.