Neural-network processing in machine learning applications relies on layer synchronization. This is practiced even in artificial Spiking Neural Networks (SNNs), which are touted as consistent with neurobiology, in spite of processing in the brain being in fact asynchronous. A truly asynchronous system however would allow all neurons to evaluate concurrently their threshold and emit spikes upon receiving any presynaptic current. Omitting layer synchronization is potentially beneficial, for latency and energy efficiency, but asynchronous execution of models previously trained with layer synchronization may entail a mismatch in network dynamics and performance. We present and quantify this problem, and show that models trained with layer synchronization either perform poorly in absence of the synchronization, or fail to benefit from any energy and latency reduction, when such a mechanism is in place. We then explore a potential solution direction, based on a generalization of backpropagation-based training that integrates knowledge about an asynchronous execution scheduling strategy, for learning models suitable for asynchronous processing. We experiment with two asynchronous neuron execution scheduling strategies in datasets that encode spatial and temporal information, and we show the potential of asynchronous processing to use less spikes (up to 50%), complete inference faster (up to 2x), and achieve competitive or even better accuracy (up to 10% higher). Our exploration affirms that asynchronous event-based AI processing can be indeed more efficient, but we need to rethink how we train our SNN models to benefit from it. (Source code available at: https://github.com/RoelMK/asynctorch)

翻译：机器学习应用中的神经网络处理依赖于层同步。这种做法即使在人工脉冲神经网络（SNNs）中也被沿用，尽管SNNs被宣传为与神经生物学原理一致，但大脑的实际处理过程本质上是异步的。一个真正的异步系统将允许所有神经元在接收到任何突触前电流时，并发地评估其阈值并发放脉冲。省略层同步在延迟和能效方面可能具有优势，但对于先前在层同步下训练的模型，异步执行可能导致网络动态与性能失配。我们提出并量化了这一问题，结果表明：在缺乏同步机制时，经层同步训练的模型要么表现不佳，要么在引入异步机制后无法获得任何能量与延迟的降低。随后，我们探索了一种基于反向传播训练泛化的潜在解决方案，该方案整合了关于异步执行调度策略的知识，以学习适用于异步处理的模型。我们在编码空间与时间信息的数据集上测试了两种异步神经元执行调度策略，结果表明异步处理能够使用更少的脉冲（最高减少50%）、更快地完成推理（最高提速2倍），并获得具有竞争力甚至更优的准确率（最高提升10%）。我们的研究证实，基于事件的异步AI处理确实可以更高效，但我们需要重新思考如何训练SNN模型以从中获益。（源代码位于：https://github.com/RoelMK/asynctorch）