Machine Learning and Control Theory for Computer Architecture

MCAt Tutorial at MICRO 2019 (October 13th) , Columbus, Ohio
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Aim and Overview

The aim of this tutorial is to inspire computer architecture researchers about the ideas of combining control theory and machine learning to design efficient computer systems.

Computer architecture is in an exciting state --- hardware is heterogeneous, reconfigurable, and distributed, and the space of applications is rapidly expanding. A major challenge is how to control and reconfigure these complex systems so that they operate in the most efficient mode. Many of the current control techniques are based on heuristics. While heuristics are simple to implement, they do not generalize and there are many examples of heuristics that work well on one system producing bad results on new systems. Fortunately, Machine Learning and Control Theory are two principled tools for architects to address the challenge of dynamically configuring complex systems for efficient operation. These techniques have the potential to provide more general and portable solutions for system management. However, there is limited knowledge within the computer architecture community regarding how control theory can help and how it can be combined with machine learning.

What can audience expect to learn?

This tutorial will familiarize architects with control theory and its combination with machine learning, so that architects can easily build computers based on these ideas. Our tutorial will present important techniques from machine learning and control theory, their systems-level abstractions, and their architectural intuitions.

Tentative schedule
Relevant papers