AI 自动设计芯片预取器，居然跑赢了 DPC4 冠军方案，单核低带宽下提速 4.6%，搞硬件架构的值得看看。
ArchAgent v2 将自动化微架构搜索扩展到多级数据预取。它引入级联进化搜索，按缓存层级依次进化和冻结预取器，并加入硬件可实现性反馈回路，在进化过程中实时估算硬件规模。在 DPC4 规则下，ArchAgent v2 自动设计的三级预取器比人工设计的冠军方案表现更好，几何平均 IPC 较基线提升 3.8%，较前冠军 BertiGO 提升 0.3%。在低带宽单核配置下，其性能提升达 4.6%，而 BertiGO 仅为 2.6%。
ArchAgent v2: A Case Study with the Data Prefetching Championship
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.