Adaptive Fission: Post-training Encoding for Low-latency Spike Neural Networks
Yizhou Jiang, Tianren Zhang, Yihan Li, Yuqian Liu, Haichuan Gao, Ying Fang, Feng Chen
Spiking Neural Networks often rely on rate coding, where high-precision inference requires 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 dynamic precision and threshold allocation based on the needs of individual neurons, while incurring minimal spatial overhead. As a generalized form of population coding, it applies broadly to pretrained SNN architectures and does not require additional training or fine-tuning. Experiments on neuromorphic hardware demonstrate up to 80\% reductions in latency and power consumption without sacrificing performance.