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

ORACLE框架使用LLM和RL进行多目标模拟电路设计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-06 04:00

研究人员开发了ORACLE，一个使用多目标强化学习优化模拟电路设计的新框架。与先前将多个目标简化为单一奖励的方法不同，ORACLE采用向量值学习和偏好感知条件化来准确捕捉帕累托权衡。这使得单个训练模型能够在不重新训练的情况下生成多样化设计，并由偏好向量引导。该系统还整合了大型语言模型来过滤次优动作，显著缩短了运行时间并改善了设计规格。 AI

影响这项研究可能通过利用先进的AI技术，显著加速和提高模拟电路设计的效率。

排序理由这是一篇研究论文，详细介绍了用于模拟电路设计优化的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi · 2026-08-06 04:00

ORACLE：基于多目标强化学习的模拟电路设计优化器，结合大语言模型引导的探索

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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ORACLE：基于多目标强化学习的模拟电路设计优化器，结合大语言模型引导的探索

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