Stepwise Weighted Spike Coding for Deep Spiking Neural Networks

Abstract

Spiking Neural Networks (SNNs) seek to mimic the spiking behavior of biological neurons and are expected to play a key role in the advancement of neural computing and artificial intelligence. The efficiency of SNNs is often determined by the neural coding schemes. Existing coding schemes either cause huge delays and energy consumption or necessitate intricate neuron models and training techniques. To address these issues, we propose a novel Stepwise Weighted Spike (SWS) coding scheme to enhance the encoding of information in spikes. This approach compresses the spikes by weighting the significance of the spike in each step of neural computation, achieving high performance and low energy consumption. A Ternary Self-Amplifying (TSA) neuron model with a silent period is proposed for supporting SWS-based computing, aimed at minimizing the residual error resulting from stepwise weighting in neural computation. Our experimental results show that the SWS coding scheme outperforms the existing neural coding schemes in very deep SNNs, and significantly reduces operations and latency.