EE Colloquia: Neuromorphic Computing: Bridging the gap between Nanoelectronics, Neuroscience and Machine Learning

Abstract: While research in designing brain-inspired algorithms have attained a stage where such Artificial Intelligence platforms are being able to outperform humans at several cognitive tasks, an often-unnoticed cost is the huge computational expenses required for running these algorithms in hardware. Recent explorations have also revealed several algorithmic vulnerabilities of deep learning systems like adversarial susceptibility, lack of explainability, catastrophic forgetting, to name a few. Bridging the computational and algorithmic efficiency gap necessitates the exploration of hardware and algorithms that provide a better match to the computational primitives of biological processing – neurons and synapses, and which require a significant rethinking of traditional von-Neumann based computing. This talk reviews recent developments in the domain of neuromorphic computing paradigms from an overarching system science perspective with an end-to-end co-design focus from computational neuroscience and machine learning to hardware and applications.  Such neuromorphic systems can potentially provide significantly lower computational overhead in contrast to standard deep learning platforms, especially in sparse, event-driven application domains with temporal information processing.

Bio: Dr. Abhronil Sengupta is the Joseph R. and Janice M. Monkowski Career Development Assistant Professor in the School of Electrical Engineering and Computer Science at Penn State. He received the PhD degree in Electrical and Computer Engineering from Purdue University in 2018 and the B.E. degree from Jadavpur University, India in 2013. He worked as a DAAD (German Academic Exchange Service) Fellow at the University of Hamburg, Germany in 2012, and as a graduate research intern at Circuit Research Labs, Intel Labs in 2016 and Facebook Reality Labs in 2017. He is pursuing an inter-disciplinary research agenda at the intersection of hardware and software across the stack of sensors, devices, circuits, systems and algorithms for enabling low-power event-driven cognitive intelligence; the ultimate goal is to bridge the gap between Nanoelectronics, Neuroscience and Machine Learning. He has published over 80 articles in referred journals and conferences and holds 4 granted/pending US patents. He serves on the IEEE Circuits and Systems Society Technical Committee on Neural Systems and Applications, Editorial Board of Scientific Reports, Neuromorphic Computing and Engineering, Frontiers in Neuroscience journals and the Technical Program Committee of several international conferences. He has been awarded the IEEE Circuits and Systems Society (CASS) Outstanding Young Author Award (2019), IEEE SiPS Best Paper Award (2018), Schmidt Science Fellows Award nominee (2017), Bilsland Dissertation Fellowship (2017). He is a member of the IEEE Electron Devices Society (EDS), Magnetics Society and Circuits and Systems (CAS) Society, the Association for Computing Machinery (ACM) and the American Physical Society (APS).