Speaker: Carole Jean Wu, Arizona State University and Facebook
Title: Designing Performance and Energy Efficiency Acceleration for Machine Learning
Computing touches almost all aspects of our modern-day lives---from gene sequencing to physics simulations to powering the Internet, from real-time image and voice recognition to predicting stock market trends. The programmability advancement of high-performance accelerators, such as graphics processors, has enabled a large diverse set of general-purpose algorithms to enjoy performance acceleration on GPU-enabled heterogeneous systems. Our recent research efforts in this domain address the strong performance and energy efficient scalability challenge faced in the post Moore’s law era.
In addition to high capacity computing, personalized computing has gained increasing attention. In this talk, I will talk about the design challenges for personalized computing at the edge and present our research efforts for mobile performance quality, temperature and energy efficiency optimization with statistical performance prediction and advanced cooling technologies. Our statistical modeling framework for application execution time are highly accurate, within 99.34% of the measured values. We show that, on an actual Qualcomm Snapdragon 8074 mobile chipset, the proposed energy efficiency controller achieves a 29% power saving over commonly-used governors while maintaining an average web page load time of 2 seconds with a likelihood of 90%. This work serves as a strong foundation and paves the way for our future work to realize machine learning inference at the edge.
Carole-Jean Wu is an Associate Professor of Computer Science and at Arizona State University (ASU). She is spending her academic sabbatical leave as a Research Scientist with Facebook’s AI Infrastructure. She is the associate site director of the NSF I/UCRC Center for Embedded Systems (CES) and holds an affiliated faculty appointment in EECE at ASU. She is a senior member of both ACM and IEEE.
Prof. Wu works in the area of Computer and System Architectures. In particular, her research interests include high-performance and energy-efficient computer architecture through hardware heterogeneity, energy harvesting techniques for emerging computing devices, temperature and energy management for portable electronics, performance characterization, analysis and prediction, and memory subsystem designs. She is the recipient of the 2018 IEEE ITHERM Best Paper Award, the 2017 NSF CAREER Award, the 2017 IEEE Young Engineer of the Year Award, the 2014 IEEE Best of Computer Architecture Letter Award, the 2013 Science Foundation Arizona Bisgrove Early Career Scholarship, and the 2011-12 Intel Ph.D. Fellowship. Her research has been supported by both industry sources and the National Science Foundation, to a level over $1.8 million.
Prof. Wu serves as the Program Chair for the IEEE International Symposium on Workload Characterization, 2018. She also serves on the Executive Committee of the IEEE Technical Committee on Computer Architecture and the Steering Committee of the IEEE International Symposium on Performance Analysis of Systems and Software, among many other program committees, including ISCA, MICRO, and HPCA. Prof. Wu completed her M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2008 and 2012, respectively. She received a B.Sc. degree in Electrical and Computer Engineering from Cornell University.