Abstract

Computer architecture design and research is often inefficient and ad hoc due to the significant costs of hardware simulators. We must urgently address these costs as technology scaling presents greater challenges in design complexity, energy efficiency, and system integration. I present the case for statistical inference in architectural design, enabling holistic solutions that (1) control complexity using inference, (2) extract efficiency using hardware specialization, and (3) analyze interaction within integrated systems using modular models. Throughout, inferential models act as surrogates for simulators and capture the complexity of simulated architectures with the speed of analytical equations. This speed transforms the way architects reason about design priorities: energy efficiency, microarchitectural adaptivity, chip multiprocessors, and multiprocessor heterogeneity.

Speaker Biography

Benjamin Lee is a postdoctoral researcher in the Computer Architecture Group at Microsoft Research. Dr. Lee earned his B.S. (2004) in electrical engineering and computer science from the University of California at Berkeley and his S.M. (2006), Ph.D. (2008) in computer science from Harvard University. His thesis was nominated by Harvard for the ACM doctoral dissertation award. Dr. Lee's research focuses on power-efficient computing and statistical inference applied to applications, architectures, and circuits. He is also interested in the policy, economics, and technology of IT environmental sustainability.