TQ-TTFS: High-Accuracy and Energy-Efficient Spiking Neural Networks Using Temporal Quantization Time-to-First-Spike Neuron

In recent years, spiking neural networks (SNNs) have gained attention for their biological realistic and event-driven characteristics, which align well with neuromorphic hardware. Time-to-First-Spike (TTFS) coding is an coding scheme for SNNs, where neurons are fired only once throughout the inference process, reducing the number of spikes and improving energy efficiency. However, the SNNs with TTFS coding face the issue of low classification accuracy. This paper introduces TQ-TTFS, a temporal quantization on TTFS neuron model to address this issue. In addition, the temporal quantization neurons can apply lower clock frequency without increasing inference latency, which can lead to higher energy efficiency. The experimental results show the effectiveness of the proposed temporal quantization neuron model in improving both classification accuracy and energy efficiency. In our simulations TQ-TTFS achieves classification accuracy of 98.6% on MNIST dataset and 90.2% on FashionMNIST dataset which are among SOTA of temporal coding SNNs. An analysis is also given to show that TQ-TTFS on an example SNN can have $2.94 \times$ energy efficiency improvement compared with tranditional TTFS coding.