Rethinking Pretraining as a Bridge from ANNs to SNNs

• URL: http://arxiv.org/abs/2203.01158v3
• Date: Fri, 4 Mar 2022 03:07:22 GMT
• Title: Rethinking Pretraining as a Bridge from ANNs to SNNs
• Authors: Yihan Lin, Yifan Hu, Shijie Ma, Guoqi Li, Dongjie Yu
• Abstract summary: Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features. How to obtain a high-accuracy model has always been the main challenge in the field of SNN.
• Score: 13.984523794353477
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spiking neural networks (SNNs) are known as a typical kind of brain-inspired models with their unique features of rich neuronal dynamics, diverse coding schemes and low power consumption properties. How to obtain a high-accuracy model has always been the main challenge in the field of SNN. Currently, there are two mainstream methods, i.e., obtaining a converted SNN through converting a well-trained Artificial Neural Network (ANN) to its SNN counterpart or training an SNN directly. However, the inference time of a converted SNN is too long, while SNN training is generally very costly and inefficient. In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism. We believe that the proposed paradigm is a more efficient pipeline for training SNNs. The pipeline includes pipeS for static data transfer tasks and pipeD for dynamic data transfer tasks. SOTA results are obtained in a large-scale event-driven dataset ES-ImageNet. For training acceleration, we achieve the same (or higher) best accuracy as similar LIF-SNNs using 1/10 training time on ImageNet-1K and 2/5 training time on ES-ImageNet and also provide a time-accuracy benchmark for a new dataset ES-UCF101. These experimental results reveal the similarity of the functions of parameters between ANNs and SNNs and also demonstrate the various potential applications of this SNN training pipeline.

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