We show how to infer deterministic cache replacement policies using off-the-shelf automata
learning and program synthesis techniques. For this, we construct and chain two …

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 …

Cache hierarchies are increasingly non-uniform and difficult to manage. Several techniques,
such as scratchpads or reuse hints, use static information about how programs access data …

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 …

We address the problem of improving cache predictability and performance in embedded
systems through the use of software-assisted replacement mechanisms. These mechanisms …

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 …

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 …

The sensitivity of a cache replacement policy expresses to what extent the execution history
may influence the number of cache hits and misses during program execution. We present …

Due to the increasing gap between CPU and memory speed, cache performance plays an
increasingly critical role in determining the overall performance of microprocessor systems …

Statistical cache models are powerful tools for understanding application behavior as a
function of cache allocation. However, previous techniques have modeled only the average …