Prof. Helen Li of Duke University

Following technology advances in high performance computation systems and fast growth of data acquisition, machine learning, especially deep neural networks (DNNs), made remarkable success in many research areas and applications. Such a success, to a great extent, is enabled by developing large-scale network models that learn from a huge volume of data. The deployment of such a big model, however, is both computation-intensive and memory-intensive. Though the research on hardware acceleration for neural network has been extensively studied, the progress of hardware development still falls far behind the upscaling of DNN models at software level. The holistic co-design across algorithm, circuit, and device levels emerges more important for execution acceleration, energy efficiency, and design flexibility. In this presentation, we will present our studies on how to optimize the training process for sparse and low-precision network models for general platforms. We will also discuss the memristor-based computing engine designs optimized for DNN inference and training.

Biography:

Hai “Helen” Li is the Clare Boothe Luce Professor and Associate Chair of the Department of Electrical and Computer Engineering at Duke University. She received her B.S and M.S. from Tsinghua University and Ph.D. from Purdue University. At Duke, she co-directs Duke University Center for Computational Evolutionary Intelligence and NSF IUCRC for Alternative Sustainable and Intelligent Computing (ASIC). 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. Dr. 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. Dr. Li is a Distinguished Lecturer of the IEEE CAS society (2018-2019) and a distinguished speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award, TUM-IAS Hans Fischer Fellowship from Germany, ELATE Fellowship, eight best paper awards and another nine best paper nominations. Dr. Li is an IEEE fellow and a distinguished member of the ACM.