Efficient Spiking Neural Networks with Radix Encoding

• URL: http://arxiv.org/abs/2105.06943v2
• Date: Thu, 2 Nov 2023 15:10:24 GMT
• Title: Efficient Spiking Neural Networks with Radix Encoding
• Authors: Zhehui Wang, Xiaozhe Gu, Rick Goh, Joey Tianyi Zhou, Tao Luo
• Abstract summary: Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) In this paper, we propose a radix encoded SNN with ultra-short spike trains. Experiments show that our method demonstrates 25X speedup and 1.1% increment on accuracy, compared with the state-of-the-art work on VGG-16 network architecture and CIFAR-10 dataset.
• Score: 35.79325964767678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to its event-driven computation mechanism and replacement of energy-consuming weight multiplications with additions. However, in order to reach accuracy of its ANN counterpart, it usually requires long spike trains to ensure the accuracy. Traditionally, a spike train needs around one thousand time steps to approach similar accuracy as its ANN counterpart. This offsets the computation efficiency brought by SNNs because longer spike trains mean a larger number of operations and longer latency. In this paper, we propose a radix encoded SNN with ultra-short spike trains. In the new model, the spike train takes less than ten time steps. Experiments show that our method demonstrates 25X speedup and 1.1% increment on accuracy, compared with the state-of-the-art work on VGG-16 network architecture and CIFAR-10 dataset.

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