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Source note2 min read

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

Source

arXiv

arxiv.org/abs/2608.04999v1

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Plain-Language Brief

The paper presents ORACLE, a multi-objective reinforcement learning-based framework for optimizing analog circuit design. The framework addresses limitations of existing methods by using vector-valued learning and preference-aware conditioning, allowing for diverse trade-off settings without retraining. It also incorporates a large language model (LLM)-guided action selection mechanism to improve efficiency. Results show significant reductions in runtime and improved figure of merit compared to state-of-the-art approaches.

What the Document Says

  • The paper introduces ORACLE, an RL-based framework for multi-objective analog circuit design optimization.
  • ORACLE replaces scalar reward optimization with vector-valued learning and preference-aware conditioning.
  • It uses a preference vector to specify the relative weights of multiple objectives.
  • The framework allows a single trained model to generate designs across diverse trade-off settings without retraining.
  • Two preference-guidance strategies are proposed: normalized-weight guidance and cosine-aligned guidance.
  • An LLM-guided action selection mechanism is incorporated for improved efficiency.
  • Results show ORACLE reduces runtime by 20.4x - 104.4x compared to state-of-the-art methods on multiple circuit topologies with 2,000 test cases.
  • It meets 99.9% of the 2,000 target specifications and achieves 5.1x - 318.6x better figure of merit.

Key Concepts Mentioned

  • Reinforcement Learning
  • Analog Circuit Design
  • Multi-Objective Optimization
  • Large Language Models
  • Preference-aware Conditioning

Claims Made by the Document

  • ORACLE is a more efficient multi-objective analog circuit design optimizer compared to state-of-the-art methods.
  • The use of vector-valued learning and preference-aware conditioning improves the ability to capture trade-offs among competing objectives.

Evidence Used

  • Results showing reduced runtime and improved figure of merit

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort. However, many existing RL-based methods focus on single-objective optimization. Even methods designed for multi-objective (MO) problems often reduce multiple design specifications to a single scalar reward. This simplification limits the ability to capture the true Pareto trade-off among competing objectives and often leads to suboptimal designs. Moreover, requiring the model to be retrained from scratch whenever the desired MO specifications change remains a key limitation. To address these challenges, we present ORACLE, an open-source RL-based framework for MO analog circuit design optimization that replaces scalar reward optimization with vector-valued learning and preference-aware conditioning. ORACLE represents a true MO analog circuit design optimizer that uses a preference vector to specify the relative weights of multiple objectives, enabling a single trained model to generate designs across diverse trade-off settings without retraining. We further propose two preference-guidance strategies, namely normalized-weight guidance and cosine-aligned guidance, to improve convergence. In addition, we incorporate a large language model (LLM)-guided action selection mechanism to filter actions that are likely to lead to suboptimal designs or increased runtime. Our results show that, on multiple circuit topologies with 2,000 test cases, ORACLE reduces runtime by 20.4x - 104.4x compared to state-of-the-art approaches. It also meets 99.9% of the 2,000 target specifications, and achieves 5.1x - 318.6x better figure of merit in the resulting output specs.