面向可穿戴设备的二阶脉冲状态空间模型 (ASecond-Order SpikingSSM for Wearables)

Spiking neural networks have garnered increasing attention due to their energy efficiency, multiplication-free computation, and sparse event-based processing. In parallel, state space models have emerged as scalable alternatives to transformers for long-range sequence modelling by avoiding quadratic dependence on sequence length. We propose SHaRe-SSM (Spiking Harmonic Resonate-and-Fire State Space Model), a second-order spiking SSM for classification and regression on ultra-long sequences. SHaRe-SSM outperforms transformers and first-order SSMs on average while eliminating matrix multiplications, making it highly suitable for resource-constrained applications. To ensure fast computation over tens of thousands of time steps, we leverage a parallel scan formulation of the underlying dynamical system. Furthermore, we introduce a kernel-based spiking regressor, which enables the accurate modelling of dependencies in sequences of up to 50k steps. Our results demonstrate that SHaRe-SSM achieves superior long-range modelling capability with energy efficiency (52.1x less than ANN-based second order SSM), positioning it as a strong candidate for resource-constrained devices such as wearables

翻译：脉冲神经网络因其高能效、无乘法计算和基于稀疏事件的特性而日益受到关注。与此同时，状态空间模型通过避免对序列长度的二次依赖，已成为长程序列建模中可扩展的Transformer替代方案。我们提出了SHaRe-SSM（脉冲谐波谐振放电状态空间模型），这是一种面向超长序列分类与回归任务的二阶脉冲状态空间模型。SHaRe-SSM在平均性能上优于Transformer和一阶状态空间模型，同时消除了矩阵乘法运算，使其特别适用于资源受限的应用场景。为确保在数万个时间步长上的快速计算，我们采用了底层动力系统的并行扫描公式。此外，我们引入了一种基于核函数的脉冲回归器，能够对长达5万步序列中的依赖关系进行精确建模。实验结果表明，SHaRe-SSM在保持卓越长程建模能力的同时实现了显著的能效提升（能耗比基于人工神经网络的二阶状态空间模型降低52.1倍），这使其成为可穿戴设备等资源受限场景的理想选择。