Keynotes

Addressing Challenges of Core Microarchitecture Research

by Daniel A. Jiménez

Abstract

Core microarchitecture research has been studied for decades, but remains crucial due to the evolving demands of modern computing workloads. Growing instruction footprints, the influx of massive data into the processor, the overhead of modern programming languages, and the emphasis on productivity over performance all require innovative approaches. As Moore’s Law reaches its end, the onus of improving performance and efficiency falls on microarchitecture research. Additionally, with more and more companies opting to design their own processors, academia is tasked not only with developing new processing technologies but also training the workforce to design these new chips.

In this talk, I will motivate the need for continued core microarchitecture research, give some recent examples of topics we study such as instruction fetch, address translation, and cache management, and give some insight into the challenges we face in this kind of work. For example, branch prediction has been a well-studied topic for decades, but recent trends in software design have caused huge growth in instruction footprints, putting pressure on other areas of instruction fetch as well as overwhelming the capacity of modern branch predictors and ultimately leading to performance degradation.

Bio

Daniel A. Jiménez is a Professor in the Department of Computer Science and Engineering at Texas A&M University. Jiménez received his Ph.D. in Computer Sciences from the University of Texas at Austin. Jiménez’s research is in microarchitecture, including microarchitectural prediction and cache management. He pioneered the development of neural-inspired branch predictors that have been implemented in millions of processors sold by IBM, AMD, Oracle, and Samsung.

He designed the neural branch predictors used in the popular Samsung Galaxy S7/8/9/10/20. His 2001 paper on perceptron-based branch prediction won the “HPCA Test of Time Award” in 2019. Jiménez won the 2021 IEEE CS B. Ramakrishna Rau Award for contributions to neural branch prediction. Jiménez is an IEEE Fellow, an ACM Distinguished Scientist, an NSF CAREER award winner, and member of the ISCA, MICRO, and HPCA halls of fame. He is the Chair of the IEEE Computer Society Technical Committee on Computer Architecture (TCCA) and co-Chair of the ISCA Steering Committee. He was General Chair of IEEE HPCA in 2011, Program Chair for IEEE HPCA in 2017, and Selection Committee Chair for IEEE Micro “Top Picks” 2020.