Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits, & Systems

Department of Electical Engineering Seminar

Re-Engineering Computing with Neuro-Inspired Learning: Devices, Circuits, & Systems

Kaushik Roy
Professor, Purdue University

Abstract: Advances in machine learning, notably deep learning, have led to computers matching or surpassing human performance in several cognitive tasks including vision, speech and natural language processing. However, implementation of such neural algorithms in conventional "von-Neumann" architectures are several orders of magnitude more area and power expensive than the biological brain. Hence, we need fundamentally new approaches to sustain exponential growth in performance at high energy-efficiency beyond the end of the CMOS roadmap in the era of ‘data deluge’ and emergent data-centric applications. Exploring the new paradigm of computing necessitates a multi-disciplinary approach: exploration of new learning algorithms inspired from neuroscientific principles, developing network architectures best suited for such algorithms, new hardware techniques to achieve orders of improvement in energy consumption, and nanoscale devices that can closely mimic the neuronal and synaptic operations of the brain leading to a better match between the hardware substrate and the model of computation. In this talk, I will discuss our work on spintronic device structures consisting of single-domain/domain-wall motion based devices for mimicking neuronal and synaptic units. Implementation of different neural operations with varying degrees of bio-fidelity (from "non-spiking" to "spiking" networks) and implementation of on-chip learning mechanisms (Spike-Timing Dependent Plasticity) will be discussed.

Bio: Kaushik Roy is the Edward G. Tiedemann, Jr., Distinguished Professor of Electrical and Computer Engineering at Purdue University. He received his PhD from University of Illinois at Urbana-Champaign in 1990. His current research focuses on cognitive algorithms, circuits and architecture for energy-efficient cognitive computing, computing models, and neuromorphic devices. Kaushik has supervised more than 75 PhD dissertations and his students are well placed in universities and industry. He is the co-author of two books on Low Power CMOS VLSI Design (John Wiley & McGraw Hill). He currently directs the Center for Brain-Inspired Computing sponsored by SRC and DARPA.

Wednesday, November 13, 2019
4:00 p.m.
Mann Student Center
10 Hillhouse Avenue