TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers

Abstract

Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many techniques to convert trained ANNs to SNNs have been developed. However, after the conversion, a trade-off relation between accuracy and latency exists in SNNs, causing considerable latency in large size datasets such as ImageNet. We present a technique, named as TCL, to alleviate the trade-off problem, enabling the accuracy of 73.87% (VGG-16) and 70.37% (ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs.

To appear in the 58th Design Automation Conference (DAC 2021)

References in corpus (4)

• Surrogate Gradient Learning in Spiking Neural Networks
• Exploring the Connection Between Binary and Spiking Neural Networks
• DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks
• Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Cited by in corpus (1)

• Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization