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[2602.22425] ArchAgent: Agentic AI-driven Computer Architecture Discovery
Summary
ArchAgent is an AI-driven system that automates computer architecture discovery, achieving significant performance improvements in cache replacement policies without human intervention.
Why It Matters
As demand for computing power grows, ArchAgent represents a breakthrough in automating hardware design. Its ability to outperform human-developed solutions in speed and efficiency could revolutionize computer architecture research and development, highlighting the potential of agentic AI systems in various domains.
Key Takeaways
- ArchAgent automates the design of cache replacement policies, achieving state-of-the-art results.
- It demonstrated a 5.3% IPC speedup over previous benchmarks in just two days.
- The system can tune runtime parameters for specific workloads, leading to further performance improvements.
- ArchAgent operates significantly faster than traditional human-driven design processes.
- The research raises important questions about the reliability of existing microarchitectural simulators.
Computer Science > Artificial Intelligence arXiv:2602.22425 (cs) [Submitted on 25 Feb 2026] Title:ArchAgent: Agentic AI-driven Computer Architecture Discovery Authors:Raghav Gupta, Akanksha Jain, Abraham Gonzalez, Alexander Novikov, Po-Sen Huang, Matej Balog, Marvin Eisenberger, Sergey Shirobokov, Ngân Vũ, Martin Dixon, Borivoje Nikolić, Parthasarathy Ranganathan, Sagar Karandikar View a PDF of the paper titled ArchAgent: Agentic AI-driven Computer Architecture Discovery, by Raghav Gupta and 12 other authors View PDF HTML (experimental) 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 workloads, it generated a policy in just 18 days ...
