Presentation - A Journey into Neuromorphic Computing: Models, Algorithms, and Implementations

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 neurons 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.

Biography - Qinru Qiu

Dr. Qinru Qiu received her PhD in Electrical Engineering from University of Southern California in 2001. She is currently a professor in the Department of Electrical Engineering and Computer Science at Syracuse University.

Dr. Qiu has more than 20 years of research experience in machine intelligence and more than 15 years’ experience in neuromorphic computing. She is a recipient of NSF CAREER award in 2009, IEEE Region 1 Technological Innovation award in 2020, and ACM Distinguished Member in 2022.

She serves as an associate editor for IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Circuit and Systems Magazine, IEEE Transactions on Cognitive and Developmental Systems, and Frontier on Neuroscience on Neuromorphic Engineering. She has also served as a technical program committee member of many conferences including DAC, ICCAD, ISLPED, DATE, etc.

She is the director of the NSF I/UCRC (Industry University Collaborative Research Center) ASIC (Alternative Sustainable and Intelligent Computing) Center Syracuse Site.