Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding

TL;DR

This paper develops training methods for deep spiking neural networks using time-to-first-spike coding, significantly reducing spikes and energy consumption while maintaining performance.

Contribution

It introduces novel training techniques including stochastic relaxation, regularization, and batch normalization tailored for TTFS-coded deep SNNs to enhance energy efficiency.

Findings

Achieved comparable accuracy with fewer spikes.

Reduced energy consumption in deep SNNs.

Validated effectiveness of proposed training methods.

Abstract

The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the human brain, have been studied as prominent energy-efficient neural networks. Due to their event-driven and spatiotemporally sparse operations, SNNs show possibilities for energy-efficient processing. To unlock their potential, deep SNNs have adopted temporal coding such as time-to-first-spike (TTFS)coding, which represents the information between neurons by the first spike time. With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency. Several studies have successfully introduced TTFS coding in deep SNNs, but they showed restricted efficiency improvement owing to the lack of consideration for efficiency during training. To address the aforementioned issue, this paper presents training methods for energy-efficient deep SNNs with TTFS coding. We introduce a surrogate DNN model to train the deep SNN in a feasible time and analyze the effect of the temporal kernel on training performance and efficiency. Based on the investigation, we propose stochastically relaxed activation and initial value-based regularization for the temporal kernel parameters. In addition, to reduce the number of spikes even further, we present temporal kernel-aware batch normalization. With the proposed methods, we could achieve comparable training results with significantly reduced spikes, which could lead to energy-efficient deep SNNs.