Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
Summary:This research proposes a novel method for generating high-quality and diverse RTL circuits using Large Language Models (LLMs) and multi-objective reinforcement learning (RL). Unlike traditional approaches that optimize a single circuit, this method focuses on optimizing the quality and diversity of the entire circuit set in the PPA (Power, Performance, Area) objective space. The proposed method reformulates the coverage improvement of the Pareto solution set into a sequential circuit generation
Technical Mechanism Analysis
The innovation of this research lies in combining multi-objective reinforcement learning (RL) with Large Language Models (LLMs) to generate Pareto-optimal RTL circuits. Traditional methods typically aggregate PPA (Power, Performance, Area) objectives into a single scalar reward function, making it difficult to optimize multiple objectives simultaneously. This often results in circuits that perform well in some aspects but poorly in others. The proposed method addresses this issue through the following approaches:
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Generation-based Training Pipeline: The coverage improvement of the entire Pareto solution set is reformulated into a sequential circuit generation problem. This allows the model to generate a series of circuits that achieve diverse trade-offs across different PPA objectives.
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Reward Function based on Hypervolume Contribution (HVC): HVC measures the contribution of each generated circuit to the Pareto front. This method effectively guides the model to generate circuits that are closer to the Pareto front, thereby improving the overall quality and diversity of the circuit set.
Engineering Trade-offs and Performance
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Strengths: The method demonstrates superior performance on the RTLLM v2 benchmark, significantly improving the coverage of the Pareto front. Compared to non-PPA optimization baselines, the relative Hypervolume increased by 13.3%; compared to single-objective RL methods, it increased by 14.4%. Additionally, the method excels in circuit diversity, providing designers with more high-quality circuit candidates.
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Challenges: While the method achieves significant progress in diversity and quality, the complexity of multi-objective optimization remains high, especially when dealing with large-scale circuit designs. The demand for computational resources may increase significantly. Furthermore, dynamically adjusting the target weights based on different product requirements and design constraints is an area that requires further research.
Developer Deployment Recommendations
For RTL circuit design practitioners, this research offers a new approach to better balance multiple objectives during the design process. Developers are advised to consider the following:
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Model Training and Tuning: In practical applications, developers need to thoroughly train and tune the model according to specific design requirements and constraints to ensure that the generated circuit set meets the actual application needs.
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Resource Management: Due to the high computational resource requirements of multi-objective optimization, developers need to manage resources effectively. When necessary, they can use technologies such as distributed computing or mixed-precision training to improve efficiency.
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Tool Integration: It is recommended to integrate this research method with existing EDA tools to achieve a more efficient circuit design workflow.
— END —Source: ArXiv AI (cs.AI) (2026-10-21)
Tags: #Large Language Models #Multi-Objective Reinforcement Learning #RTL Circuit Design #PPA Optimization #Pareto Front
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