15 April 2024 2:00 -3:00 PM EDT/+4 UTC 18:00-19:00

The proliferation of "big data" applications poses significant challenges in terms of speed and scalability for traditional computer systems. The increasing performance gap between CPUs and memory, commonly referred to as the "memory wall," greatly impedes the performance of traditional Von Neumann machines. As a result, neuromorphic computing systems have garnered considerable attention. These systems operate by emulating the charging and discharging processes of neurons and synapse potential in a biologically plausible computing paradigm. Electrical impulses or spikes facilitate inter-neuron communication. The unique encoding of information in the spike domain enables asynchronous event-driven computation and communication, potentially resulting in high energy efficiency. In this seminar, I will introduce several typical computing models of neuron and synapses that can be utilized to build spiking neural networks (SNNs). Additionally, selected inference and learning algorithms for SNNs will be discussed, followed by a brief overview of existing hardware and software solutions for implementing neuromorphic computing. I will further present our Error-Modulated Spike-Timing-Dependent Plasticity (EMSTDP) algorithm, which is capable of supervised training of a deep SNN, and its implementation on a neurosynaptic processor. Compelling results that highlight the potential of this innovative computing paradigm will be presented.