Autonomous PCB Floorplanning via Reinforcement Learning Agents
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Solution Overview
Problem
The design of printed circuit boards (PCBs) and packages remains largely manual and human-dependent, limiting quality and time to market due to the lack of automation in electronic design automation (EDA) systems.
Innovation Solution
A computer-implemented method using hierarchical reinforcement learning agents to iteratively recommend and update electronic design actions, such as placement, via, and route actions, to enhance PCB design automation, incorporating neural networks for approximating board routeability as a fitness or reward function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human effort is used for PCB design, then design quality can be maintained through human judgment, but productivity is limited and time to market increases
Solution Approach 1:
The system enables autonomous PCB design where the machine learning agent independently performs floorplanning, placement, via insertion, and routing decisions without continuous human intervention. The agent learns from training data and autonomously optimizes design parameters, eliminating the need for manual human effort while maintaining design quality through learned expertise.
Solution Approach 2:
The patent replaces manual human mechanical design processes with an automated machine learning system. The reinforcement learning agent substitutes human judgment and manual manipulation with algorithmic decision-making, using trained models to predict optimal design actions and automatically update the PCB layout without human physical interaction.
2Device complexity
If manual design processes are used, then design complexity can be managed through human expertise, but automation extent remains low
Solution Approach 1:
The machine learning agent autonomously manages complex PCB design tasks including floorplanning, component placement, via insertion, and routing. The system independently evaluates design states, selects optimal actions based on learned policies, and iteratively improves the design without human intervention, achieving high automation while managing complexity through intelligent algorithms.
Solution Approach 2:
The machine learning agent acts as an intermediary between design requirements and PCB implementation. It translates high-level design specifications into detailed placement and routing decisions, mediating between abstract design goals and concrete physical implementation while managing complexity through learned transformation rules.
3Productivity
If hierarchical reinforcement learning agents are used for autonomous design, then productivity and automation are improved, but device complexity of the system increases
Solution Approach 1:
The autonomous design system is segmented into hierarchical reinforcement learning agents, each responsible for specific PCB design tasks such as floorplanning, placement, via insertion, and routing. This segmentation allows complex design problems to be divided into manageable sub-tasks, with each agent specializing in particular aspects while working together to achieve complete design automation.
Solution Approach 2:
The system employs dynamic hierarchical reinforcement learning agents that can adapt their behavior based on the current design state. The agents dynamically select actions from available options, adjust their strategies based on feedback from the design environment, and evolve their policies through learning, enabling flexible automation that handles varying design complexities.
Data Source
AI summary
The present disclosure relates to systems and methods for floorplanning using machine learning techniques. Embodiments may include receiving an electronic design and analyzing the electronic design using a reinforcement learning agent. Embodiments may further include recommending a first action wherein the first action includes at least one of a place agent action, a via agent action, or a route agent action. Embodiments may also include updating the electronic design based upon, at least in part, the first action to generate an updated electronic design. Embodiments may further include analyzing the updated electronic design using the reinforcement learning agent and recommending a second action wherein the second action includes at least one of a place agent action, a via agent action, or a route agent action. Embodiments may also include updating the updated electronic design based upon the second action to generate a second updated electronic design.