When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency
Abstract: Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to convolutional neural networks (CNN), thanks to advanced ANN-to-SNN conversion techniques, all while delivering remarkable energy and latency efficiency when deployed on neuromorphic hardware. However, these conversion techniques incur a large number of time steps, and consequently, high spiking activity. In this paper, we propose a novel ANN-to-SNN conversion framework, that incurs an exponentially lower number of time steps compared to that required in the existing conversion approaches. Our framework modifies the standard integrate-and-fire (IF) neuron model used in SNNs with no change in computational complexity and shifts the bias term of each batch normalization (BN) layer in the trained ANN. To reduce spiking activity, we propose training the source ANN with a fine-grained $\ell_1$ regularizer with surrogate gradients that encourages high spike sparsity in the converted SNN. Our proposed framework thus yields lossless SNNs with low latency, low compute energy, thanks to the low time steps and high spike sparsity, and high test accuracy, for example, $75.12$% with only $4$ time steps on the ImageNet dataset. Codes will be made available. Code is available at https://github.com/godatta/SNN_meets_ANN.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: We have revised the submission in response to the latest comment from reviewer vBf2, who noted that some references were still incorrect. We have corrected those references and thoroughly reviewed the entire bibliography to ensure all entries are now accurate.
Code: https://github.com/godatta/SNN_meets_ANN
Assigned Action Editor: ~Tatsuya_Harada1
Submission Number: 4055
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