Abstract
Spiking neural networks (SNNs) that enable greater computational efficiency on neuromorphic hardware have attracted attention. Existing ANN-SNN conversion methods can effectively convert the weights to SNNs from a pre-trained ANN model. However, the state-of-the-art ANN-SNN conversion methods suffer from accuracy loss and high inference latency due to ineffective conversion methods. To solve this problem, we train low-latency SNN through knowledge distillation with Kullback-Leibler divergence (KL divergence). We achieve superior accuracy on CIFAR-100, 74.42% for VGG16 architecture with 5 timesteps. To our best knowledge, our work performs the fastest inference without accuracy loss compared to other state-of-the-art SNN models.
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