Abstract
Despite extensive developments in improving cache hit rates, designing an optimal cache replacement policy that mimics Belady’s algorithm still remains a challenging task. Existing standard static replacement policies does not adapt to the dynamic nature of memory access patterns, and the diversity of computer programs only exacerbates the problem. Several factors affect the design of a replacement policy such as hardware upgrades, memory overheads, memory access patterns, model latency, etc. The amalgamation of a fundamental concept like cache replacement with advanced machine learning algorithms provides surprising results and drives the development towards cost-effective solutions. In this paper, we review some of the machine-learning based cache replacement policies that outperformed the static heuristics.
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