EECS Seminar: AI Models for Edge Computing - Hardware-aware Optimizations for Efficiency

Abstract:  As artificial intelligence (AI) transforms various industries, state-of-the-art models have exploded in size and capability. The growth in AI model complexity is rapidly outstripping hardware evolution, making the deployment of these models on edge devices challenging. To enable advanced AI locally, models must be optimized for fitting into the hardware constraints. In this presentation, we will first discuss how computing hardware designs impact the effectiveness of commonly used AI model optimizations for efficiency, including techniques like quantization and pruning. Additionally, we will present several methods, such as hardware-aware quantization and structured pruning, to demonstrate the significance of software/hardware co-design. We will also demonstrate how these methods can be understood via a straightforward theoretical framework, facilitating their seamless integration in practical applications and their straightforward extension to distributed edge computing. At the conclusion of our presentation, we will share our insights and vision for achieving efficient and robust AI at the edge.

Biography:  Hai “Helen” Li is the Clare Boothe Luce Professor and Department Chair of the Electrical and Computer Engineering Department at Duke University. She received bachelor's and master's degrees from Tsinghua University and a doctorate from Purdue University. Her research interests include neuromorphic circuit and system for brain-inspired computing, machine learning acceleration and trustworthy AI, conventional and emerging memory design and architecture, and software and hardware co-design. Li served/serves as the associate editor for multiple IEEE and ACM journals. She was the general chair or technical program chair of multiple IEEE/ACM conferences and the Technical Program Committee members of over 30 international conference series. Li is a distinguished lecturer of the IEEE CAS Society (2018-2019) and a distinguished speaker of ACM (2017-2020). Li is a recipient of the NSF Career Award, DARPA Young Faculty Award, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, nine best paper awards and the Ten Year Retrospective Influential Paper Award on ICCAD 2023. Li is a fellow of ACM and IEEE.