Events Calendar

ECE Seminar: The Role of Machine Learning in Electronic Design Automation

Speaker: Vidya A. Chhabria, Ph.D. Candidate, Electrical and Computer Engineering Department, University of Minnesota

Talk Title: The Role of Machine Learning in Electronic Design Automation

Abstract: For several decades, advances in hardware, accelerated by Moore's law and enabled by electronic design automation (EDA) tools, have sustainably met the demands for high computation at low energy and cost. However, emerging applications demand computing power far beyond today's system capabilities. Rapid advances in high-performance computing address the problem by using accelerators for specialized tasks such as machine learning (ML), increasing design diversity and system complexity. With Moore's law running out of steam, EDA tools now play a crucial role in meeting these computational demands. EDA tools are challenged to build chips that not only compensate for slow down in scaling, but also provide high performance for both ML and non-ML applications, which use a variety of new architectural techniques and operate under stringent performance constraints. Conventional EDA tools involve computationally expensive analysis and optimizations and are suboptimal as they often tradeoff speed for accuracy. ML promises to address these challenges as it has found tremendous success in solving these problems in classification, detection, and design space exploration problems in several different applications.

In this talk, I will show how leveraging ML techniques can revolutionize EDA tools by addressing the existing challenges. In particular, the talk will focus on tools that aid designers in (i) delivering power inside the chip without significant losses to meet power demands and (ii) sending the heat outside the chip to avoid high temperatures. The first section of the talk will show how a fast ML inference brings down several hours of runtime to a few milliseconds on industry-scale designs for these tasks. The second section will demonstrate how ML enables high-quality solutions through rapid optimizations. A key challenge with the proposed ML-based methods is the limited availability of open-source data and benchmarks for training and evaluation. The third section will show how ML can generate synthetic training sets and benchmarks for evaluating novel EDA solutions to these tasks. I will conclude by presenting avenues for future research in ML and EDA.

Biography: Vidya A. Chhabria is a Ph.D. candidate in the Electrical and Computer Engineering department at the University of Minnesota. She received her B.E. in Electronics and Communication from M. S. Ramaiah Institute of Technology, India, in 2016, and her M.S. in Electrical Engineering from the University of Minnesota in 2018. Her research interests are in the areas of electronic design automation, IC design, and machine learning. She has interned at Qualcomm Technologies, Inc. in the summer of 2017 and NVIDIA Corporation during the summers of 2020 and 2021. She received the ICCAD Best Paper Award in 2021, the University of Minnesota Doctoral Dissertation Fellowship in 2021, Louise Dosdall Fellowship in 2020, and Cadence Women in Technology Scholarship in 2020.


Host: Dr. Pierluigi Nuzzo, nuzzo@usc.edu

Webcast: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

WebCast Link: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

Audiences: Everyone Is Invited

Contact: Mayumi Thrasher