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  • title: An Imitation Learning Approach for Cache Replacement
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            An Imitation Learning Approach for Cache Replacement
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            An Imitation Learning Approach for Cache Replacement

            Jul 12, 2020

            Speakers

            EZL

            Evan Zheran Liu

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            MH

            Milad Hashemi

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            KS

            Kevin Swersky

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            About

            Program execution speed critically depends on reducing cache misses, as cache misses are orders of magnitude slower than hits. To reduce cache misses, we focus on the problem of cache replacement: choosing which cache line to evict upon inserting a new line. This is challenging because it requires planning far ahead and currently there is no known practical solution. As a result, current replacement policies typically resort to heuristics designed for specific common access patterns, which fail…

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            ICML 2020

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            About ICML 2020

            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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