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MicroEvo: Knowledge-Guided LLM Sampling for Efficie... | AI Research

Key Takeaways

  • MicroEvo is a framework designed to optimize microarchitecture design space exploration (DSE) by combining the reasoning capabilities of Large Language Model...
  • Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making.
  • Existing methods perform blind search without considering microarchitectural dependencies and fail to learn from the iterative search effectively, leading to wasted evaluations and weak Pareto convergence.
  • In this paper, we propose MicroEvo, a knowledge-guided framework that couples off-the-shelf LLMs with Monte Carlo Tree Search (MCTS) for multi-objective microarchitecture optimization.
  • Experiments show that MicroEvo improves Pareto-front quality by up to 36.2% over NSGA-II and achieves 10.6x higher search efficiency, and also demonstrates strong scalability to a complex industrial-scale core.
Paper AbstractExpand

Microarchitecture design space exploration suffers from expansive search spaces and expensive PPA evaluation, leaving only a small simulation budget for design decision-making. Existing methods perform blind search without considering microarchitectural dependencies and fail to learn from the iterative search effectively, leading to wasted evaluations and weak Pareto convergence. In this paper, we propose MicroEvo, a knowledge-guided framework that couples off-the-shelf LLMs with Monte Carlo Tree Search (MCTS) for multi-objective microarchitecture optimization. MicroEvo combines LLM-driven evolutionary operators, a Pareto-aware tree policy that balances Pareto contribution and diversity, an active knowledge accumulation mechanism that extracts and reuses optimization insights, and state-aware directives that adapt the search behavior online. Experiments show that MicroEvo improves Pareto-front quality by up to 36.2% over NSGA-II and achieves 10.6x higher search efficiency, and also demonstrates strong scalability to a complex industrial-scale core. The code repository is available at: this https URL .

MicroEvo is a framework designed to optimize microarchitecture design space exploration (DSE) by combining the reasoning capabilities of Large Language Models (LLMs) with the structured search of Monte Carlo Tree Search (MCTS). The researchers, including Jia Xiong, Runkai Li, and others, developed this approach to address the high dimensionality of processor design spaces and the high cost of performance, power, and area (PPA) evaluations, which often lead to inefficient, blind search patterns in traditional methods.

Improving Search Efficiency

Microarchitecture design involves a vast combinatorial space where small parameter changes can significantly impact performance. Traditional methods often rely on blind probabilistic sampling, which wastes limited simulation budgets on suboptimal configurations. MicroEvo replaces this with an informed evolutionary search. By integrating LLMs into an MCTS framework, the system uses architectural knowledge to propose designs that respect dependencies between components, such as matching fetch widths with reorder buffer capacities. This allows the framework to achieve up to 10.6x higher search efficiency compared to NSGA-II.

Core Methodology

The framework operates through four primary components:

  • Pareto-UCT: A decision policy that selects nodes for expansion based on both their contribution to the global Pareto front and their diversity within the objective space. This ensures the search balances exploitation of known high-quality regions with exploration of under-sampled areas.

  • LLM-Driven Operators: The system uses two specific operators—the "knowledge tuner" for local refinements based on historical insights, and the "pattern explorer" for identifying higher-level design strategies by analyzing sibling configurations.

  • Active Knowledge Accumulation (AKA): This mechanism extracts and stores optimization insights from parent-child design pairs. It maintains a memory of useful rules, allowing the LLM to retrieve and apply successful design patterns in later iterations.

  • State-Aware Directives (SAD): This component monitors the progress of the search and dynamically adjusts the LLM’s behavior between "exploit," "balanced," and "explore" modes to prevent the search from stalling in local optima.

Performance and Results

According to the authors, MicroEvo improves Pareto-front quality by up to 36.2% over NSGA-II. The framework is designed to be scalable, showing effectiveness even when applied to complex, industrial-scale processor cores. By utilizing LLMs to interpret PPA feedback rather than treating them as black-box numerical optimizers, the framework maintains a more consistent and productive optimization trajectory throughout the limited simulation budget.

Considerations

The effectiveness of MicroEvo relies on the LLM’s ability to navigate the design space without hallucinating invalid configurations; the framework addresses this by mapping out-of-bounds parameters to the nearest valid values. While the framework significantly improves upon traditional evolutionary algorithms, its performance is tied to the quality of the initial design space definition and the ability of the LLM to translate PPA feedback into meaningful architectural adjustments. The authors note that the framework is specifically intended for scenarios where evaluation budgets are scarce and every simulation must contribute to identifying better PPA trade-offs.

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