Ultra-low-latency deep spiking neural network based on rate coding

XIONG Zhi-min; CHEN Yun-hua; FENG Ren; CHEN Ping-hua

doi:10.7641/CTA.2024.30012

Abstract：Spiking neural network(SNN)possesses robust capabilities for spatiotemporal information representation and asynchronous event processing.However,training SNN is challenging due to the non-differentiable nature of the spiking process.Converting artificial neural network(ANN)to SNN can yield deep SNN with high inference accuracy,but this approach often results in increased latency and power consumption in SNN.To mitigate network latency and power consumption,we have analyzed the primary reasons behind the loss of SNN accuracy at ultra-low latency,focusing on the asynchronous transfer characteristics of spikes.We introduce the concept of residual membrane potential error(RMPE)to address these issues.We have analyzed and derived the relationship between residual membrane potential and both the initial membrane potential and weights.Based on this understanding,we propose a layer-by-layer calibration algorithm for adjusting initial membrane potential and weights,aiming to reduce residual membrane potential error.This approach resolves the discrepancy between the assumption of a uniform distribution of spike input trains and the actual distribution.We propose a two-stage conversion framework for ANN-to-SNN conversion.In the first stage,we employ a quantization clipping activation function with a trainable stratification threshold.This allows us to train the ANN twice,optimizing both quantization error and clipping error.In the second stage,we further fine-tune the SNN to reduce residual membrane potential errors at ultra-low latency.Experimental results demonstrate that our proposed method outperforms existing methods in terms of inference latency and power consumption.