Md Sakib Hasan, Ph.D. of Univ. of Mississippi

Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to the traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture, and algorithm. As the energy consumption in complex machine learning tasks keeps increasing exponentially due to larger data sets and resource-constrained edge devices becoming increasingly ubiquitous, neuromorphic computing approaches can be a viable alternative to a deep convolutional neural network that is dominating the field today. In this talk, I introduce neuromorphic computing, outline a few representative examples from different layers of the design stack (devices, circuits, and algorithms) and conclude with a few important challenges and opportunities in this field.

Biography:

Md Sakib Hasan joined the Department of Electrical and Computer Engineering at the University of Mississippi as an assistant professor in the Fall of 2019. He received his B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology in Dhaka, Bangladesh in 2009 and his Ph.D. degree in Electrical Engineering from the University of Tennessee, Knoxville in 2017. His research interests include neuromorphic computing. secure nanoelectronic circuit design, nonlinear dynamics, semiconductor device modeling, and VLSI circuit design. 

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