This paper introduces SpikeFit, a novel training method for Spiking Neural Networks (SNNs) that enables efficient inference on neuromorphic hardware, considering all its stringent requirements: the number of neurons and synapses that can fit on a single device, and lower bit-width representations (e.g., 4-bit, 8-bit). Unlike conventional compressing approaches that address only a subset of these requirements (limited numerical precision and limited number of neurons in the network), SpikeFit treats the allowed weights' discrete values themselves as learnable parameters co-optimized with the model, allowing for optimal Clusterization-Aware Training (CAT) of the model's weights at low precision (2-, 4-, or 8-bit) which results in higher network compression efficiency, as well as limiting the number of unique synaptic connections to a value required by neuromorphic processor. This joint optimization allows SpikeFit to find a discrete weight set aligned with hardware constraints, enabling the most complete deployment across a broader range of neuromorphic processors than existing methods of SNN compression support. Moreover, SpikeFit introduces a new hardware-friendly Fisher Spike Contribution (FSC) pruning method showing the state-of-the-art performance. We demonstrate that for spiking neural networks constrained to only four unique synaptic weight values (M = 4), our SpikeFit method not only outperforms state-of-the-art SNNs compression methods and conventional baselines combining extreme quantization schemes and clustering algorithms, but also meets a wider range of neuromorphic hardware requirements and provides the lowest energy use in experiments.

翻译：本文提出SpikeFit，一种面向脉冲神经网络（SNNs）的新型训练方法，旨在满足神经形态硬件的严苛约束条件——单芯片可容纳的神经元与突触数量限制以及低位宽表示（如4位、8位）——从而实现高效推理。与仅针对部分约束（有限数值精度或有限神经元数量）的传统压缩方法不同，SpikeFit将权重离散值本身视为可与模型协同优化的可学习参数，实现了低精度（2位、4位或8位）下模型权重的集群感知训练（CAT），从而在提升网络压缩效率的同时，将独特突触连接数限制在神经形态处理器所需范围内。这种联合优化使SpikeFit能够找到符合硬件约束的离散权重集，相较于现有SNN压缩方法，可在更广泛的神经形态处理器上实现更完整的部署。此外，SpikeFit提出了一种硬件友好的Fisher脉冲贡献度（FSC）剪枝方法，展现出最先进的性能。实验表明，对于仅允许四种独特突触权重值（M = 4）的脉冲神经网络，我们的SpikeFit方法不仅优于最先进的SNN压缩方法及结合极端量化方案与聚类算法的传统基线，还能满足更广泛的神经形态硬件要求，并在实验中实现最低能耗。