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ORACLE framework uses LLMs and RL for multi-objective analog circuit design

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ORACLE, a novel framework for optimizing analog circuit design using multi-objective reinforcement learning. Unlike previous methods that simplify multiple objectives into a single reward, ORACLE employs vector-valued learning and preference-aware conditioning to accurately capture Pareto trade-offs. This allows a single trained model to generate diverse designs without retraining, guided by preference vectors. The system also incorporates large language models to filter suboptimal actions, significantly reducing runtime and improving design specifications. AI

IMPACT This research could significantly accelerate and improve the efficiency of analog circuit design by leveraging advanced AI techniques.

RANK_REASON This is a research paper detailing a new framework for analog circuit design optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ORACLE framework uses LLMs and RL for multi-objective analog circuit design

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This is a research paper detailing a new framework for analog circuit design optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi ·

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

    arXiv:2608.04999v1 Announce Type: cross Abstract: 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 design…

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