Spiking Neural Networks: Learning Algorithms and Hardware Acceleration

Spiking neural networks (SNN), a class of brain-inspired models of computation, are well equipped with spatiotemporal computing power critical for a wide range of applications. Moreover, recent advancements in neuromorphic computing have led to large-scale industrial neuromorphic chips with promising ultra-low energy event-driven data processing capability. Nevertheless, major challenges are yet to be conquered to make spike-based computation a competitive choice for real-world applications. In this talk, first, I will present techniques for tackling major challenges in training complex SNNs by developing biologically plausible learning mechanisms and error backpropagation (BP) operating on top of spiking discontinuities. Second, SNN hardware accelerators, which provide an efficient dedicated computing platform for processing spiking workloads, will be discussed.

Speaker: Peng Li, UC Santa Barbara

This lecture may also be available online.  See the weblink for Zoom information.