让脉冲神经网络训练提速44倍，同时精确计算脉冲时间。
Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks
- 用并行关联扫描一次性处理多个脉冲，打破串行限制。
- 实现高精度可微分脉冲时间求解，无需离散时间步。
- 在四个事件数据集上验证，可在GPU上端到端训练。
连续时间、事件原生的脉冲神经网络（SNN）严格基于脉冲事件运行，将脉冲时序与顺序作为信息表征而非时间离散化的副产物。这一机制符合生物计算规律，并与事件传感器和类脑处理器的原生分辨率一致，同时使计算与内存开销随事件数量增长。然而，两个挑战制约了实际可训练的事件基SNN系统：1）精确的电荷-放电-重置动态导致输入脉冲必须串行处理；2）需在无时间分箱的情况下精确求解脉冲时间。本文提出双重解决方案：首先，采用并行关联扫描技术，一次性处理多个输入脉冲，实现相较于串行模拟高达44倍的速度提升，同时保留精确的硬重置动态；其次，构建可微分脉冲时间求解器，以机器精度计算脉冲时间，无需离散时间近似或限制性解析假设。我们在四组事件数据集上验证了该方法的可行性，证明其可在GPU上实现端到端训练。
原文摘要 · Abstract (English)
Continuous-time, event-native spiking neural networks (SNNs) operate strictly on spike events, treating spike timing and ordering as the representation rather than an artifact of time discretization. This viewpoint aligns with biological computation and with the native resolution of event sensors and neuromorphic processors, while enabling compute and memory that scale with the number of events. However, two challenges hinder practical, end-to-end trainable event-based SNN systems: 1) exact charge--fire--reset dynamics impose inherently sequential processing of input spikes, and 2) precise spike times must be solved without time bins. We address both. First, we use parallel associative scans to consume multiple input spikes at once, yielding up to 44x speedups over sequential simulation while retaining exact hard-reset dynamics. Second, we implement differentiable spike time solvers that compute spike times to machine-precision without discrete-time approximations or restrictive analytic assumptions. We demonstrate the viability of training SNNs using our solutions on four event-based datasets on GPUs.
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