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2008
IEEE

Self-Optimizing Memory Controllers: A Reinforcement Learning Approach

9 years 8 months ago
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
Efficiently utilizing off-chip DRAM bandwidth is a critical issue in designing cost-effective, high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver relatively low performance in part because they often employ fixed, rigid access scheduling policies designed for average-case application behavior. As a result, they cannot learn and optimize the long-term performance impact of their scheduling decisions, and cannot adapt their scheduling policies to dynamic workload behavior. We propose a new, self-optimizing memory controller design that operates using the principles of reinforcement learning (RL) to overcome these limitations. Our RL-based memory controller observes the system state and estimates the long-term performance impact of each action it can take. In this way, the controller learns to optimize its scheduling policy on the fly to maximize long-term performance. Our results show that an RL-based memory controller improves the performance of a...
Engin Ipek, Onur Mutlu, José F. Martí
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where ISCA
Authors Engin Ipek, Onur Mutlu, José F. Martínez, Rich Caruana
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