Event
ECE Colloquium Series - Sahil Shah, UMD
Friday, September 4, 2026
3:30 p.m.
Jeong H. Kim Engineering Building, Room 1110
Darcy Long
301 405 3114
dlong123@umd.edu
Speaker: Sahil Shah, Assistant Professor, University of Maryland
Title: Devices, Circuits, and Algorithms for Energy-Efficient Processing
Abstract: The rapid growth of artificial intelligence and neural networks has created an increasing demand for energy efficient computing, particularly for applications at the edge where power, memory, and communication resources are limited. Addressing these challenges requires innovation across the computing stack, from emerging devices and circuit architectures to algorithms that can effectively exploit the underlying hardware. In this talk, I will present our efforts toward developing energy efficient computing platforms through a codesign of devices, circuits, systems, and algorithms. From the device perspective, I will discuss results from our group on emerging nonvolatile memory technologies, including ReRAM and ferroelectric devices, and their potential for computation and storage. From the circuit and system perspective, I will present our work on spike based neuromorphic architectures and data converter-less computing tiles that seek to reduce the energy and data movement overheads associated with conventional computing approaches. From the algorithm perspective, I will discuss hardware aware methods that account for the characteristics and constraints of the underlying devices and circuits. Together, these efforts illustrate how innovations across multiple layers of the computing stack can enable more efficient processing for future artificial intelligence and edge computing systems.
Bio: Sahil Shah is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He officially joined the UMD ECE Department in Spring 2021. His area of expertise is low-power analog and mixed-signal systems for energy-efficient computation. His lab investigates and designs low-power systems that can compute efficiently in a low-resource environment, such as implantable or wearable platforms. Prior to his arrival at the University of Maryland, Sahil was a postdoctoral associate in the department of Electrical Engineering at California Institute of Technology. At Caltech, he pursued research on developing robust brain-machine interface for enabling patients to control prosthetic devices. He received his PhD in Electrical Engineering from Georgia Institute of Technology in 2018 where he developed reconfigurable mixed-signal neural networks for monitoring vital and physiological signals. In 2014, he received M.Sc. from Arizona State University for developing CMOS based biosensors for monitoring pH in cell-culture media. His research interests fall into three major areas: Energy-Efficient integrated circuits, Embedded Machine learning, and Bio-Sensing and Monitoring. Sahil's long term goal is to develop robust and energy-efficient devices that will equip physicians with tools to make better diagnosis, tailor rehabilitation process for patients and technology that will help us better understand physiological and neurological activity.
