Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization

Abstract

Computer Architecture research has traditionally required the exploration of vast, highly complex design spaces through manual human effort. Large Language Models (LLMs) offer an opportunity to accelerate this process by enabling fast and automated implementation and testing of different architectural mechanisms and their combinations. We introduce Agentic Architect, a framework for agentic CPU microarchitectural design-space exploration and optimization. The Agentic Architect combines LLM-driven code evolution with cycle-accurate simulation and hardware cost modeling to explore performance, area, and power tradeoffs. Starting from a seed design, the LLM is prompted to explore the design space by implementing changes, guided by prior evaluation feedback. Each new design is compiled, simulated on training workloads, evaluated against the optimization objective, and constrained by area and power budgets. Its score and diagnostics update the candidate population and guide subsequent iterations. We evaluate the Agentic Architect framework across prefetching, cache replacement, and branch prediction. On 213 traces spanning five benchmark suites, its evolved prefetchers lead the evaluated L1D and L2 designs, delivering up to 6.7% and 2.6% additional speedup, respectively, over the strongest state-of-the-art design, while the best evolved cache replacement design improves over Mockingjay by up to 1.7%. Moreover, across 191 branch-prediction traces, the evolved branch predictor achieves 97.62% of TAGE-SC-L's geometric mean IPC at 10.58% of its area. Our analysis highlights the importance of seed selection, hardware constraints, and training workloads in shaping search outcomes. Overall, Agentic Architect establishes the first end-to-end framework for agentic CPU microarchitectural design optimization, opening a new direction for architecture research.