When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency

Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter Anthony Beerel

Keywords: SNN, ANN-to-SNN conversion, IF model, ImageNet, spiking activity

TL;DR: In this paper, we propose a novel ANN-to-SNN conversion framework for ultra low-latency and energy efficiency using a new integrate-and-fire (IF) neuron model.

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.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 11507