[2608.13027] Why Do Prefetchers Fail? Let Agents Answer

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arXiv:2608.13027 (cs)

[Submitted on 13 Aug 2026]

Title:Why Do Prefetchers Fail? Let Agents Answer

Authors:Xiangfeng Sun, Ceyu Xu, Ningzhi Ai, Zeyu Zhu, Yiyang Yuan, Yuan Xie<br>View a PDF of the paper titled Why Do Prefetchers Fail? Let Agents Answer, by Xiangfeng Sun and 5 other authors

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Abstract:Hardware prefetchers are crucial to processor performance, yet their design remains labor-intensive and expert-driven. Architects inspect execution and memory-access traces, identify patterns, translate them into online hardware heuristics, and evaluate them in simulation, often with no guarantee of improvement. Human experts cannot systematically inspect billion-instruction traces across diverse real-world workloads.

We present a performance-anomaly-driven autoresearch flow that repeatedly asks why a deployed prefetcher fails and uses the diagnoses to construct the Mixture of Prefetchers (MoP). Each iteration localizes high-impact unexplained misses to program counters, gives agents hardware logs, source code, and sliced traces, validates diagnoses through runnable minimal cases, and synthesizes specialized sub-prefetchers for recurring pattern families. Measured performance and remaining anomalies feed subsequent iterations, enabling simulator-in-the-loop discovery beyond model priors.

The campaign consumes 1.91 billion DeepSeek V4 Pro tokens. On SPEC CPU2006 and SPEC CPU2017, MoP achieves a 61.1% geomean IPC speedup over no prefetching, outperforming the human-designed Alecto, Berti, and Pythia prefetchers by 14.5%, 21.6%, and 23.6%, respectively. RTL synthesis in a 6nm library reports 110 KB of on-chip storage and 0.0347 mm^2 area. To our knowledge, this is the first empirical demonstration that an agent-driven hardware-design process can produce an RTL-practical prefetcher that outperforms state-of-the-art human designs on unseen workloads.

Subjects:

Hardware Architecture (cs.AR)

Cite as:<br>arXiv:2608.13027 [cs.AR]

(or<br>arXiv:2608.13027v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2608.13027

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Xiangfeng Sun [view email]<br>[v1]<br>Thu, 13 Aug 2026 09:54:38 UTC (564 KB)

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