Machine Learning for Performance and Power Modeling/Prediction
Prof. Lizy Kurian John
Department of Electrical and Computer Engineering
The University of Texas at Austin
ljohn@ece.utexas.edu
Effective design space exploration relies on fast and accurate pre-silicon performance and power models. Simulation is commonly used for understanding architectural tradeoffs, however many emerging workloads cannot even run on many full-system simulators. Even if you manage to run an emerging workload, it may be a tiny part of the workload, because detailed simulators are prohibitively slow. This talk presents some examples of how machine learning can be used to solve some of the problems haunting the performance evaluation field.
An application for machine learning is in cross-platform performance and power prediction. If one model is too slow to run real-world benchmarks/workloads, is it possible to predict/estimate its performance/power by using runs on another platform? Are there correlations that can be exploited using machine learning to make cross-platform performance and power predictions? A methodology to perform cross-platform performance/power predictions will be presented in this talk. Another application illustrating the use of machine learning to calibrate analytical power estimation models will be discussed.
Yet another application for machine learning has been to create max power stressmarks. Manually developing and tuning so called stressmarks is extremely tedious and time-consuming while requiring an intimate understanding of the processor. In our past research, we created a framework that uses machine learning for the automated generation of stressmarks. In this talk, the methodology of the creation of automatic stressmarks will be explained. Experiments on multiple platforms validating the proposed approach will also be described.
Bio: Lizy Kurian John is B. N. Gafford Professor in the Electrical and Computer Engineering at UT Austin. She received her Ph. D in Computer Engineering from the Pennsylvania State University. Her research interests include workload characterization, performance evaluation, architectures with emerging memory technologies, and high performance processor architectures for emerging workloads. She is a recipient of an NSF CAREER award, the University of Texas Austin Engineering Foundation Faculty Award, the Halliburton, Brown and Root Engineering Foundation Young Faculty Award 2001, and the University of Texas Alumni Association (Texas Exes) Teaching Award 2004, and was chosen as The Pennsylvania State University Outstanding Engineering Alumnus 2011, etc. She has coauthored 200+ papers in the computer architecture/performance evaluation fields, a book on Digital Systems Design using VHDL (Cengage Publishers, 2016, 2007), a book on Digital Systems Design using Verilog (Cengage Publishers, 2014) and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. She holds 10 US patents and is a Fellow of IEEE.