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CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency — arXiv:2610.07668

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07668
License (as archived): CC0-1.0
Archived: 2026-10-07T11:21:00.988Z
SHA-256 of the archived text: 65b1c372c8dd7b00b8644fb74ed9d88c599355e425b12b0ab70b8f957bca01f1
Stable URL of this page: https://metraipolis.com/documento/dc_c8bd45900dcc

How to cite

CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency — arXiv:2610.07668 — arXiv (en), https://arxiv.org/abs/2610.07668 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8bd45900dcc (Metraipolis archive dc_c8bd45900dcc, sha256 65b1c372c8dd7b00b8644fb74ed9d88c599355e425b12b0ab70b8f957bca01f1)

BibTeX

@misc{dcc8bd45900dcc,
  title = {CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency — arXiv:2610.07668},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07668},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8bd45900dcc; sha256 65b1c372c8dd7b00b8644fb74ed9d88c599355e425b12b0ab70b8f957bca01f1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07668. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engineered predictors, or offline imitation models that cannot generate new decision logic on their own. At the same time, replacement is shaped by the causal interaction of prefetching, thrashing, spatial locality, and access-type behavior, producing an enormous design space that is difficult to traverse manually. Prior approaches typically rely on heuristics, parameter tuning, or imitation of an offline optimal policy, capturing correlations rather than synthesizing new mechanisms. As a result, their performance gains often plateau and they overfit under dynamic workload conditions. CACHEFORGE is the first framework to evolve cache-replacement policies end-to-end by embedding a large language model inside a governed hardware-aware loop. In each iteration, the LLM proposes new C++ replacement logic, the policy is evaluated under a trace-based CRC-2 ChampSim simulator, and the framework enforces feasibility through reward shaping, structural checks, dynamic mutation, temperature scheduling, and cross-policy crossover. This closed-loop generation-evolution loop specifically designed for cache replacement policy enables the discovery of compact policies that satisfy hardware constraints while exploring algorithmic transformations beyond fixed predictor structures. Across SPEC CPU2006, CACHEFORGE outperforms all CRC-2 baselines. It improves the total hit rate by 27.36%, 19.69%, 13.72%, 13.15%, 11.83%, and 5.73% over MPPPB, ReD, Hawk-eye, SHiP++, LIME, and LRU, respectively. On memory-intensive workloads, it increases IPC by 10.15%, 7.89%, 6.34%, 3.64%, 3.12%, and 2.71% over LRU, MPPPB, LIME, ReD, SHiP++, and Hawkeye.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Hardware Architecture (cs.AR) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.07668 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:24.749Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).