目标驱动的PCB合成使用机器学习和云规模计算

Taylor Hogan
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引用次数: 0

摘要

X AI是一个基于云的系统,利用机器学习和搜索,使用基于物理的分析和高级设计来放置和布线印刷电路板。我们提出了一种基于反馈的蒙特卡罗树搜索(MCTS)算法来探索可能的设计空间。当MCTS了解到可能的解决方案时,给出了一个或多个度量来评估设计的质量。在探索过程中训练策略和价值网络,以学习准确地权重质量动作并识别有用的设计状态。这是作为与其他前馈工具的放置和路由的反馈循环一起执行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Goal Driven PCB Synthesis Using Machine Learning and CloudScale Compute
X AI is a cloud-based system that leverages machine learning, and search to place and route printed circuit boards using physics-based analysis and high-level design. We propose a feedback-based Monte Carlo Tree Search (MCTS) algorithm to explore the space of possible designs. A metric, or metrics, is given to evaluate the quality of designs as MCTS learns about possible solutions. A policy and value network are trained during exploration to learn to accurately weight quality actions and identify useful design states. This is performed as a feedback loop in conjunction with other feedforward tools for placement and routing.
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