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Reconfigurable and Self-Optimizing Multicore Architectures

Event Date: April 30, 2009
Speaker: Dr Engin Ipek
Speaker Affiliation: Microsoft Research,
Computer Architecture Group
Sponsor: ECE Faculty Candidate
Time: 11:00 AM
Location: EE 317
Contact Name: Prof T.N. Vijaykumar
Open To: Acceptable for ECE694A
As industry rides the transistor density growth in multicore processors
by providing more and more cores, these will exert increasing levels of
pressure on shared system resources. Efficient resource management
becomes critical to obtaining high utilization, and eliminating
potential bandwidth, latency, and cost barriers in multicore systems.
Unfortunately, current hardware policies for microarchitectural resource
management are ad hoc at best, and are generally incapable of providing
basic functionalities like anticipating the long-term consequences of
scheduling decisions (planning), or generalizing from experience
obtained through past resource allocation decisions to act successfully
in new situations (learning). As a result, current hardware controllers
tend to grossly under utilize the (already limited) platform resources
available. In this talk, using the problem of memory scheduling as
context, I will describe the use of machine learning (ML) technology in
designing self-optimizing, adaptive hardware controllers capable of
planning, learning, and continuously adapting to changing workload
demands. An ML-based design approach allows the hardware designer to
focus on what performance target the controller should accomplish and
what system variables might be useful to ultimately derive a good
control policy, rather than devising a fixed policy that describes
exactly how the controller should accomplish that target. This not only
eliminates much of the human design effort involved in traditional
controller design, but also yields higher-performing, more efficient

This work was completed as part of Engin Ipek's Ph.D. thesis at Cornell's Computer Systems Laboratory. It has been nominated for the 2008 ACM Doctoral Dissertation Award by Cornell University.

BIO:  Engin Ipek is a researcher in the Computer Architecture
group at Microsoft Research. He earned his Ph.D. (2008), M.S. (2007),
and B.S. (2003) degrees from Cornell University, all in Electrical and
Computer Engineering. His research interests are in computer
architecture, with an emphasis on multicore architectures,
hardware-software interaction, and the application of machine learning
to computer systems. He is a member of the ACM and the IEEE.