A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

• URL: http://arxiv.org/abs/2106.06984v1
• Date: Sun, 13 Jun 2021 13:20:12 GMT
• Title: A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
• Authors: Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu
• Abstract summary: Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. We show that a proper way to calibrate the parameters during the conversion of ANN to SNN can bring significant improvements.
• Score: 11.014383784032084
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the ReLU activation to spike activation while keeping the parameters intact. Perhaps surprisingly, in this work we show that a proper way to calibrate the parameters during the conversion of ANN to SNN can bring significant improvements. We introduce SNN Calibration, a cheap but extraordinarily effective method by leveraging the knowledge within a pre-trained Artificial Neural Network (ANN). Starting by analyzing the conversion error and its propagation through layers theoretically, we propose the calibration algorithm that can correct the error layer-by-layer. The calibration only takes a handful number of training data and several minutes to finish. Moreover, our calibration algorithm can produce SNN with state-of-the-art architecture on the large-scale ImageNet dataset, including MobileNet and RegNet. Extensive experiments demonstrate the effectiveness and efficiency of our algorithm. For example, our advanced pipeline can increase up to 69% top-1 accuracy when converting MobileNet on ImageNet compared to baselines. Codes are released at https://github.com/yhhhli/SNN_Calibration.

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