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Agentic Architect: An Agentic AI Framework for Arch... | AI Research

Key Takeaways

  • Agentic Architect is a new framework designed to automate the complex, time-consuming process of computer architecture design.
  • Rapid advances in Large Language Models (LLMs) create new opportunities by enabling efficient exploration of broad, complex design spaces.
  • This is particularly valuable in computer architecture, where performance depends on microarchitectural designs and policies drawn from vast combinatorial spaces.
  • We introduce Agentic Architect, an agentic AI framework for computer architecture design exploration and optimization that combines LLM-driven code evolution with cycle-accurate simulation.
  • The human architect specifies the optimization target, seed design, scoring function, simulator interface, and benchmark split, while the LLM explores implementations within these constraints.
Paper AbstractExpand

Rapid advances in Large Language Models (LLMs) create new opportunities by enabling efficient exploration of broad, complex design spaces. This is particularly valuable in computer architecture, where performance depends on microarchitectural designs and policies drawn from vast combinatorial spaces. We introduce Agentic Architect, an agentic AI framework for computer architecture design exploration and optimization that combines LLM-driven code evolution with cycle-accurate simulation. The human architect specifies the optimization target, seed design, scoring function, simulator interface, and benchmark split, while the LLM explores implementations within these constraints. Across cache replacement, data prefetching, and branch prediction, Agentic Architect matches or exceeds state-of-the-art designs. Our best evolved cache replacement design achieves a 1.062x geomean IPC speedup over LRU, 0.6% over Mockingjay (1.056x). Our evolved branch predictor achieves a 1.100x geomean IPC speedup over Bimodal, 1.5% over its Hashed Perceptron seed (1.085x). Finally, our evolved prefetcher achieves a 1.76x geomean IPC speedup over no prefetching, 17% over its VA/AMPM Lite seed (1.59x) and 21% over SMS (1.55x). Our analysis surfaces several findings about agentic AI-driven microarchitecture design. Across evolved designs, components often correspond to known techniques; the novelty lies in how they are coordinated. The architect's role is shifting, but the human remains central. Seed quality bounds what search can achieve: evolution can refine and extend an existing mechanism, but cannot compensate for a weak foundation. Likewise, objectives, constraints, and prompt guidance affect reliability and generalization. Overall, Agentic Architect is the first end-to-end open-source framework for agentic AI architecture exploration and optimization.

Agentic Architect is a new framework designed to automate the complex, time-consuming process of computer architecture design. Traditionally, architects spend years manually refining microarchitectural policies—such as how a processor predicts branches or manages cache memory—through a slow cycle of design, simulation, and manual iteration. This paper introduces an "agentic" AI approach that combines Large Language Models (LLMs) with cycle-accurate simulation to explore these vast design spaces, allowing the AI to evolve and optimize architectural policies far more efficiently than manual methods.

How the Framework Works

The system functions as an automated loop where the human architect acts as a high-level director rather than a manual designer. The architect provides the framework with a "seed" design (a starting point), a set of performance goals, and a suite of benchmark workloads. The AI agent then takes over, using an LLM to generate and mutate code for new architectural policies. These candidates are automatically compiled and tested against a cycle-accurate simulator. If a design fails to compile or runs too slowly, the system catches the error and provides feedback to the LLM, ensuring that only viable, high-performing designs move forward in the evolutionary process.

Performance Results

The framework was tested across three critical areas of processor design: cache replacement, data prefetching, and branch prediction. In all three categories, Agentic Architect produced designs that matched or outperformed existing state-of-the-art solutions. For example, its evolved cache replacement policy achieved a 1.062x speedup over the standard LRU method, and its prefetcher outperformed reference designs by 17% to 21%. These results demonstrate that the AI can discover sophisticated coordination mechanisms between known architectural techniques that human designers might overlook.

The Role of the Human Architect

Despite the automation, the paper emphasizes that the human architect remains central to the process. The quality of the final design is heavily dependent on the "seed" provided by the human; the AI can refine and extend an existing mechanism, but it cannot compensate for a weak foundation. Furthermore, the architect’s choices regarding the scoring function, the selection of training traces, and the structure of the prompt are the primary factors that determine whether the AI produces a robust, generalizable design or one that simply overfits to specific test cases.

Key Takeaways

The research highlights that the true power of LLMs in this field lies in "co-design." By offloading the iterative, combinatorial search to an AI agent, architects can focus on defining the constraints and objectives of the system. The framework is modular, meaning it can be adapted to different simulators and LLMs, making it a flexible tool for future research. As the first end-to-end open-source framework for this type of AI-driven architecture optimization, Agentic Architect provides a new, scalable path for overcoming the design bottlenecks that have emerged as Moore’s Law slows down.

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