Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks

Yizhou Jiang, Feng Chen, Yihan Li, Yuqian Liu, Haichuan Gao, Tianren Zhang, Ying Fang

Keywords: spiking neural network, ann-to-snn conversion

TL;DR: This paper introduces a post-training encoding method that reduces the number of time-steps required for spiking neural networks.

Abstract: Spiking Neural Networks (SNNs) often rely on rate coding, where high-precision inference depends on long time-steps, leading to significant latency and energy cost—especially for ANN-to-SNN conversions. To address this, we propose Adaptive Fission, a post-training encoding technique that selectively splits high-sensitivity neurons into groups with varying scales and weights. This enables neuron-specific, on-demand precision and threshold allocation while introducing minimal spatial overhead. As a generalized form of population coding, it seamlessly applies to a wide range of pretrained SNN architectures without requiring additional training or fine-tuning. Experiments on neuromorphic hardware demonstrate up to 80\% reductions in latency and power consumption without degrading accuracy.

Primary Area: Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 10236