MiCE: An ANN-to-SNN Conversion Technique to Enable High Accuracy and Low Latency

Abstract

Spiking Neural Networks (SNNs) mimic the behavior of biological neurons. Unlike traditional Artificial Neural Networks (ANNs) that operate in a continuous time domain and use activation functions to process information, SNNs operate discrete event-driven, where data is encoded and communicated through spikes or discrete events. This unique approach offers several advantages, such as efficient computation and lower power consumption, making SNNs particularly attractive for energy-constrained and neuromorphic applications. However, training SNNs poses significant challenges due to the discrete nature of spikes and the non-differentiable behavior they exhibit. As a result, converting pre-trained ANNs into SNNs has gained attention as a convenient approach. While this approach simplifies the training process, it introduces certain drawbacks, including high latency. The conversion of ANNs to SNNs typically leads to a loss of accuracy, which can be attributed to various factors, including quantization, clipping, and timing errors. Previous studies have proposed techniques to mitigate quantization and clipping errors during the conversion process. However, they do not consider timing errors, degrading SNN accuracies at low latency conditions. This work introduces the MiCE conversion method, which offers a comprehensive joint optimization strategy to simultaneously alleviate quantization, clipping, and timing errors. At a moderate latency of 8 time-steps, our converted ResNet-20 achieves classification accuracies of 79.02% and 95.74% on the CIFAR-100 and CIFAR-10 datasets, respectively.