# CacheQuery: Learning Replacement Policies from Hardware Caches

arXiv: https://arxiv.org/abs/1912.09770 · HTML: https://mlsanity.com/paper/1912.09770/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [ALARM: Active LeArning of Rowhammer Mitigations](https://mlsanity.com/paper/2211.16942/) (2022)
- [Conflict-Aware Active Automata Learning (Extended Version)](https://mlsanity.com/paper/2308.14781/) (2023)
- [Conflict-Aware Active Automata Learning](https://mlsanity.com/paper/2310.01003/) (2023)

## Abstract (arXiv)

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 abstractions that expose the cache replacement policy of any set in the cache hierarchy as a membership oracle to the learning algorithm, based on timing measurements on a silicon CPU. Our experiments demonstrate an advantage in scope and scalability over prior art and uncover 2 previously undocumented cache replacement policies.