EAGER: Deep Learning for Microarchitectural Prediction Grant uri icon


  • Computer programs often have highly predictable behavior. Microprocessors use predictors to improve program performance and efficiency. Decisions made by a program can often be predicted with good accuracy, and patterns of data usage can be predicted to improve system efficiency and performance. However, incorrect predictions can lead to poor performance or lost opportunities for improving efficiency. This project proposes to use deep learning to improve prediction in microprocessors. Deep learning is a technology that has been used to improve computer vision and other pattern recognition tasks in large computing systems, but so far it has not been applied at the very small scale and tight timing margins of improving microprocessors. The project will likely result in improved microprocessors, as well as educational, mentoring, and career opportunities for under-represented groups in computer science. The PI will incorporate the research into classroom teaching. The Ph.D. students trained through this project will enhance industrial and academic workforce. The PI will continue to recruit women and minority graduate students into his research program for this project. Outreach to under-represented groups will include PI leadership and participation at CRA-W mentoring workshops for women and minority graduate students. The goal of the proposed research is to exploit deep learning to design new microarchitectural predictors capable of exploiting previously untapped levels of predictability in program behavior to improve performance, power, and energy. Deep neural networks will be used to greatly improve the accuracy of microarchitectural predictors. This project will first explore latency-tolerant cache locality predictors, then move to control-flow prediction that has tighter timing constraints. Proposed predictors will be evaluated in a variety of contexts representing modern workloads at scales from mobile phones to datacenters. The research incurs a high-risk because no deep neural network has even been developed to operate at the sub-nanosecond level. However, the research offers a high-payoff due to the tremendous potential to improve performance. Results will be manifested through students'' theses and dissertations as well as publication in top-tier architecture venues.

date/time interval

  • 2016 - 2019