In-Memory Computing to Accelerate Machine Learning

Name: Mariam Rakka

Chair: Prof. Fadi Kurdahi

Date: May 14, 2025

Time: 11:00 AM

Location: EH 2430

Committee: Prof. Nikil Dutt, Prof. Rainer Döemer

Title: In-Memory Computing to Accelerate Machine Learning

Abstract: The increasing computational demands of Machine Learning (ML) models have made conventional computing architectures inadequate due to energy inefficiencies and data movement bottlenecks. In-memory computing (IMC) offers a promising alternative by enabling computation directly within memory arrays. This dissertation presents novel architectural and algorithmic strategies to accelerate machine learning inference using IMC. It proposes precision-fluid data flows and hardware/software co-design techniques to efficiently accelerate different ML workloads. Performance evaluations demonstrate that IMC-based designs can deliver substantial improvements in energy efficiency and throughput, making them viable candidates for next-generation AI systems.

Bio: Mariam Rakka received her B.E. degree in Computer and Communications Engineering with high distinction from the American University of Beirut, Lebanon, in 2020, along with two minors in Mathematics and Business Administration. She received her M.S. degree in Electrical and Computer Engineering from the University of California, Irvine, in 2022. She is currently pursuing her Ph.D. degree in Electrical and Computer Engineering at the University of California, Irvine under the mentorship of Professor Fadi J. Kurdahi. Her research interests include systems for machine learning and hardware/software co-design. She has completed research internships at Arm in 2022, 2023, and 2024, where she contributed to the design of future generations of ARM processors. She has published in top-tier journals and conferences and served as a reviewer for several peer-reviewed journals in the field.