Extensive research has been carried out to improve cache replacement policies, yet
designing an efficient cache replacement policy that incurs low hardware overhead remains …

In the past few decades, much research has been conducted on the design of cache
replacement policies. Prior work frequently relies on manually-engineered heuristics to …

Despite its success in many areas, deep learning is a poor fit for use in hardware predictors
because these models are impractically large and slow, but this paper shows how we can …

Memory-intensive workloads operate on massive amounts of data that cannot be captured
by last-level caches (LLCs) of modern processors. Consequently, processors encounter …

The past decade has seen the rise of highly successful cache replacement policies that are
based on binary prediction. For example, the Hawkeye policy learns whether lines loaded …

Belady's algorithm is optimal but infeasible because it requires knowledge of the future. This
paper explains how a cache replacement algorithm can nonetheless learn from Belady's …

Last-level caches mitigate the high latency of main memory. A good cache replacement
policy enables high performance for memory intensive programs. To be useful to industry, a …

The design of an effective last-level cache (LLC) in general—and an effective cache
replacement/partitioning algorithm in particular—is critical to the overall system …

Program execution speed critically depends on increasing cache hits, as cache hits are
orders of magnitude faster than misses. To increase cache hits, we focus on the problem of …

Replacement policy, one of the key factors determining the effectiveness of a cache,
becomes even more important with latest technological trends toward highly associative …