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This paper introduces a novel autoresearch flow that leverages performance-anomaly diagnosis to iteratively improve hardware prefetcher design through agent-driven processes. By analyzing billion-instruction traces and synthesizing specialized sub-prefetchers, the approach constructs a Mixture of Prefetchers (MoP) that significantly enhances instruction per cycle (IPC) performance. The results show a 61.1% geomean IPC speedup over no prefetching and outperforms existing human-designed prefetchers, marking a significant advancement in automated hardware design methodologies.
An agent-driven approach to hardware design has achieved a 61.1% IPC speedup, outperforming expert-designed prefetchers by up to 23.6%.
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.
