发表机构
Google; University of California, Berkeley; Google DeepMind（谷歌公司; 加州大学伯克利分校; 谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出ArchAgent v2框架，通过级联进化搜索和硬件可实现性反馈循环，在DPC4中自动设计出性能优于人工方案的三级预取器，为计算机架构师提供了自动化智能体发现的实用工具。
AI 中文摘要
智能体人工智能在自动化算法设计方面展现出巨大潜力，但由于搜索空间庞大、硬件预算严格以及模拟时间漫长，将类似技术扩展到计算机微架构发现领域仍具挑战性。本研究提出ArchAgent v2，这一框架将自动化微架构搜索扩展到多级数据预取场景。原始ArchAgent已在竞赛环境中成功发现单级缓存替换策略，但无法扩展到设计空间和自由度更大的多级预取任务。为解决该问题，我们为ArchAgent引入两项新改进：一是级联进化搜索，通过在各个缓存级别依次进化并冻结预取器来细分设计空间；二是硬件可实现性反馈循环，将实时大小估算直接嵌入进化过程。在第4届数据预取锦标赛（DPC4）的相同规则下评估，ArchAgent v2自动设计出的三级预取器性能优于人工设计的冠军方案，进一步证明自动化智能体发现是计算机架构师的实用工具。我们发现的策略整体上相比基线实现了3.8%的几何平均IPC加速，较此前冠军BertiGO提升0.3%；在低带宽单核配置下，该策略性能加速达4.6%，而BertiGO仅为2.6%。不过，由于模拟延迟阻碍进化速度，多核进化仍是重大挑战。最后，我们对ArchAgent进化超过12000个候选设计的分析，为自动化进化智能体如何探索和合成复杂微架构逻辑提供了关键见解。
英文摘要
Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture discovery remains challenging due to vast search spaces, strict hardware budgets, and long simulation times. In this work, we present ArchAgent v2, a framework which scales automated microarchitecture search to multi-level data prefetching. While the original ArchAgent successfully discovered single-level cache replacement policies in competition settings, it does not scale to multi-level prefetching where the design space and degrees of freedom are larger. To overcome this, we introduce two new additions to ArchAgent: a cascaded evolutionary search that subdivides the design space by sequentially evolving and freezing prefetchers at individual cache levels, and a hardware-realizability feedback loop that embeds real-time size-estimation directly into the evolution process. Evaluated under identical rules of the 4th Data Prefetching Championship (DPC4), ArchAgent v2 automatically designs a three-level prefetcher that outperforms the winning hand-designed solution, further demonstrating automated agentic discovery as a useful tool for computer architects. Our discovered policy achieves a 3.8\% geometric mean IPC speedup over the baseline overall and a 0.3\% improvement over the prior champion, BertiGO. On low-bandwidth single-core configurations, our policy yields a 4.6\% performance speedup compared to only 2.6\% for BertiGO. However, multi-core evolution still remains a significant challenge due to simulation latency impeding evolution speed. Finally, our profiling of an ArchAgent evolution of over 12,000 candidate designs provides key insights into how automated evolutionary agents explore and synthesize complex microarchitectural logic.
