Goal Driven PCB Synthesis Using Machine Learning and CloudScale Compute

Abstract

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.