ArchAgent: Agentic AI-driven Computer Architecture Discovery
Raghav Gupta, Akanksha Jain, Abraham Gonzalez, Alexander Novikov, Po-Sen Huang, Matej Balog, Marvin Eisenberger, Sergey Shirobokov, Ng\^an V\~u, Martin Dixon, Borivoje Nikoli\'c, Parthasarathy Ranganathan, Sagar Karandikar

TL;DR
ArchAgent leverages agentic AI to autonomously discover and optimize cache replacement policies, achieving state-of-the-art performance improvements in a fraction of the time required by human experts.
Contribution
This paper introduces ArchAgent, an AI-driven system that automates computer architecture design, specifically discovering new cache policies faster and more effectively than traditional human-led methods.
Findings
Achieved 5.3% IPC speedup over SoTA in two days on multi-core workloads.
Generated a new cache policy in 18 days with 0.9% IPC improvement on SPEC06 workloads.
Enabled workload-specific tuning, resulting in an additional 2.4% IPC speedup.
Abstract
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant advances in algorithm design, improving code efficiency, and enabling discovery across scientific domains. Bridging these worlds, we present ArchAgent, an automated computer architecture discovery system built on AlphaEvolve. We show ArchAgent's ability to automatically design/implement state-of-the-art (SoTA) cache replacement policies (architecting new mechanisms/logic, not only changing parameters), broadly within the confines of an established cache replacement policy design competition. In two days without human intervention, ArchAgent generated a policy achieving a 5.3% IPC speedup improvement over the prior SoTA on public multi-core Google Workload Traces. On the heavily-explored single-core SPEC06…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Embedded Systems Design Techniques · Machine Learning in Materials Science
