EE Colloquium: Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems

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. Bridging the computational efficiency gap necessitates the exploration of devices, circuits and architectures 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.

Recent experiments in spintronic technologies are revealing immense possibilities of implementing a plethora of neural and synaptic functionalities by single spintronic device structures that can be operated at very low terminal voltages. Leveraging insights from such experiments, I will present a multi-disciplinary perspective across the entire stack of devices, circuits and systems to envision the design of an "All-Spin" neuromorphic processor enabled with on-chip learning functionalities that can potentially achieve two to three orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations. I will conclude the presentation by providing my vision of enabling end-to-end cognitive intelligence across the computing stack that combines knowledge from devices and circuits to machine learning and computational neuroscience.

Biography: Dr. Abhronil Sengupta is an Assistant Professor in the School of Electrical Engineering and Computer Science at Penn State University. Dr. Sengupta 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. The ultimate goal of Dr. Sengupta’s research is to bridge the gap between Nanoelectronics and Machine Learning. 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. Dr. Sengupta has published over 45 articles in referred journals and conferences and holds 4 granted/pending US patents. He has been awarded the IEEE SiPS Best Paper Award (2018), Schmidt Science Fellows Award nominee (2017), Bilsland Dissertation Fellowship (2017), CSPIN Student Presenter Award (2015), Birck Fellowship (2013), the DAAD WISE Fellowship (2012), and his publications have featured as APL Editor’s Picks (2015) and top popular articles in TCAS-I, TCAD, TETCI, JAP and JJAP, among others. His work on spin-device based neuromorphic computing has been highlighted in media by MIT Technology Review, US Department of Defense, American Institute of Physics among others.

 

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Media Contact: Vishal Monga

 
 

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The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

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