Abstract
The primary role of a computing system is to reliably perform the tasks it is assigned. Factors
such as performance and power efficiency are meaningful only so long as the system can reliably
execute the specified computations. As Moore's Law reaches its limits, devices are becoming less
reliable and are experiencing new types of failures. Addressing these reliability challenges
efficiently is crucial to scaling to smaller technology nodes. Moreover, reliability tends to be a
significant hurdle for all emerging technologies, whether it's new memory technologies, DNA
storage, or quantum computing. Successfully overcoming the reliability challenges also plays a
crucial role in enabling the emerging technologies. In this talk, I will share our recent work on
DRAM reliability and quantum computing to show how efficiently addressing device failures can
help us push the boundaries -- whether extending Moore's Law or enabling quantum computing.
Bio
Moinuddin Qureshi is a Professor of Computer Science at Georgia Tech. His research interests include
computer architecture, hardware security, and quantum computing. Previously, he was a research scientist
at IBM T. J. Watson (2007-2011), where he developed caching algorithms for Power-7. Qureshi received
the 2022 ACM SIGARCH Maurice Wilkes Award for contributions to high-performance memory systems
and is a fellow of ACM and IEEE. His research has been recognized with several best-paper awards,
multiple inclusions at the ISCA-50 retrospective, and several "impact" awards. Qureshi is passionate about
teaching and mentoring students. Several of his former PhD advisees are faculty members at top academic
institutions (including one at UT Austin). Qureshi received the 2024 "Outstanding Doctoral Thesis
Advisor Award" at Georgia Tech. Qureshi is a Longhorn, having received his Ph.D. from UT Austin in 2007.
Abstract
Historically, single-thread CPU performance has improved steadily.
However, the relentless quest for higher performance has uncovered
fundamental limitations, particularly as chip design complexity
continues to rise. Consequently, traditional methods for enhancing CPU
performance—such as scaling the depth and width of processors—are
producing diminishing returns.
At the same time, modern data science techniques are being
successfully applied to various aspects of chip design to address this
complexity. While there have been some initiatives in this direction
in the microarchitecture community, the pace of adoption is not
keeping up with the need. The microarchitecture community must
reinvent itself by adopting modern data science methods to drive
innovation despite increasing design complexity. In this talk, I will
describe a few examples of microarchitectural features in the light of
modern data science techniques and discuss the optimization
opportunities these techniques provide that could not have been
achieved without them.
Bio
Dr. Gilles Pokam is a Senior Principal Engineer at Intel. Before
joining Intel, he was a postdoctoral researcher at UC San Diego and a
researcher at the IBM T.J. Watson Research Center in New York. His
research focuses on microarchitecture and its interactions with system
software and security.
Currently, Dr. Pokam leads efforts to develop innovative CPU
microarchitectures through the application of AI and the use of
beyond-CMOS devices. He holds a Ph.D. in Computer Science from INRIA
(France) and has been awarded over 30 patents, along with more than 50
publications at leading conferences in microarchitecture and system
software. Dr. Pokam is a two-time recipient of the IEEE Top Pick Award
and had his research selected for inclusion in the 2023 ISCA@50
25-Year Retrospective. He is also a member of the MICRO Hall of Fame.