ORACLE: RL Framework for Multi-Objective Analog Circuit Design
A new open-source framework named ORACLE has been developed by researchers for optimizing multi-objective (MO) analog circuit design using reinforcement learning (RL). Unlike traditional RL methods that condense multiple design criteria into a single scalar reward, ORACLE utilizes vector-valued learning and preference-aware conditioning. This innovative technique effectively captures the true Pareto trade-offs among various objectives, addressing a significant drawback of existing approaches. Moreover, ORACLE allows for adjustments to MO specifications without the need for complete retraining, marking a notable advancement. Detailed in a paper on arXiv (arXiv:2608.04999), this work aims to enhance automation in analog circuit design, an area where RL has demonstrated potential. The abstract emphasizes the shortcomings of single-objective optimization and promotes ORACLE's vector-based approach for managing competing factors like power, speed, and area in circuit design.
Key facts
- ORACLE is an open-source RL framework for multi-objective analog circuit design optimization.
- It replaces scalar reward optimization with vector-valued learning and preference-aware conditioning.
- The framework addresses limitations of existing RL methods that reduce multiple design specifications to a single scalar reward.
- ORACLE eliminates the need for retraining from scratch when desired MO specifications change.
- The paper is available on arXiv with identifier arXiv:2608.04999.
- The work aims to reduce manual effort in analog circuit design automation.
- ORACLE uses a preference vector to specify the relative importance of competing objectives.
- The approach captures the true Pareto trade-off among competing objectives.
Entities
Institutions
- arXiv