One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization

• URL: http://arxiv.org/abs/2403.08786v1
• Date: Tue, 30 Jan 2024 02:00:28 GMT
• Title: One-Spike SNN: Single-Spike Phase Coding with Base Manipulation for ANN-to-SNN Conversion Loss Minimization
• Authors: Sangwoo Hwang, Jaeha Kung,
• Abstract summary: As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than conventional artificial neural networks (ANNs) In this work, we propose a single-spike phase coding as an encoding scheme that minimizes the number of spikes to transfer data between SNN layers. Without any additional retraining or architectural constraints on ANNs, the proposed conversion method does not lose inference accuracy (0.58% on average) verified on three convolutional neural networks (CNNs) with CIFAR and ImageNet datasets.
• Score: 0.41436032949434404
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As spiking neural networks (SNNs) are event-driven, energy efficiency is higher than conventional artificial neural networks (ANNs). Since SNN delivers data through discrete spikes, it is difficult to use gradient methods for training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN counterparts, pre-trained ANNs are converted to SNNs (ANN-to-SNN conversion). During the conversion, encoding activations of ANNs to a set of spikes in SNNs is crucial for minimizing the conversion loss. In this work, we propose a single-spike phase coding as an encoding scheme that minimizes the number of spikes to transfer data between SNN layers. To minimize the encoding error due to single-spike approximation in phase coding, threshold shift and base manipulation are proposed. Without any additional retraining or architectural constraints on ANNs, the proposed conversion method does not lose inference accuracy (0.58% on average) verified on three convolutional neural networks (CNNs) with CIFAR and ImageNet datasets.In addition, graph convolutional networks (GCNs) are converted to SNNs successfully with an average accuracy loss of 0.90%.Most importantly, the energy efficiency of our SNN improves by 4.6~17.3 X compared to the ANN baseline.

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