PCB Synthesis Computational Requirement Prediction
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Solution Overview
Problem
Electronic Design Automation (EDA) processes are compute-intensive and time-consuming, making it difficult to predict computational requirements for synthesizing printed circuit boards and packages, especially in cloud computing environments where human interaction is minimized, and resources need to be optimally allocated to avoid unnecessary costs.
Innovation Solution
A computer-implemented method that receives PCB electronic design files, infers missing parameters using an inference engine, and trains a machine learning system with process data from placement, via assignment, routing, and metal pouring processes to predict computational requirements, allowing for optimal resource allocation in cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If EDA processes are deployed to cloud computing environments with dynamic resource allocation, then automation extent and productivity are improved, but device complexity and difficulty of detecting and measuring computational requirements worsen
Solution Approach 1:
The system performs preliminary analysis of design files to extract features and predict computational requirements before actually running EDA processes. This advance preparation includes analyzing netlists, board outlines, component libraries, and rules files to estimate resource needs, allowing cloud resources to be pre-configured optimally without requiring complex real-time adjustments during automated execution
Solution Approach 2:
The patent introduces an intermediary prediction system that sits between the design input and EDA processing. This intermediary analyzes design characteristics and translates them into computational requirement estimates, serving as a mediator that simplifies the interface between automated cloud resources and complex EDA processes without requiring direct human intervention in resource management
2Productivity
If compute resources are allocated dynamically in cloud environments, then productivity is improved, but loss of time for predicting and allocating resources worsens
Solution Approach 1:
The system extracts design features and predicts computational requirements in advance before EDA processes begin execution. By analyzing netlists, board outlines, component libraries, and rules files upfront, the system prepares resource allocation estimates that enable immediate cloud resource provisioning without delays during the actual automated processing
Solution Approach 2:
The patent replaces manual resource allocation mechanisms with an automated machine learning-based prediction system. Instead of human designers manually estimating computational requirements or using complex manual allocation procedures, the system automatically analyzes design features and predicts resource needs, significantly reducing the time required for resource planning while maintaining or improving allocation accuracy
3Manufacturing precision
If human designers manually tune EDA process parameters, then manufacturing precision and reliability are improved, but ease of operation and productivity worsen
Solution Approach 1:
The system enables self-service by allowing design files to automatically provide the information needed for computational requirement prediction. The netlists, board outlines, component libraries, and rules files contain all necessary characteristics, eliminating the need for human designers to manually input or tune parameters. The system autonomously extracts features and makes predictions based solely on the provided design data
Solution Approach 2:
The patent replaces manual parameter tuning with automated machine learning prediction. Instead of human designers manually adjusting EDA process parameters based on experience and trial-and-error, the system uses trained machine learning models to automatically predict optimal computational requirements based on design file analysis, maintaining synthesis precision while dramatically improving ease of operation
Data Source
AI summary
The present disclosure relates to electronic circuit design, and more specifically, to determining the computational requirements of fully synthesizing a printed circuit board and/or package. Embodiments may include receiving, using a processor, one or more PCB electronic design files and determining whether the PCB electronic design files include data required for a synthesis engine. If any data is missing, the method may include inferring one or more parameters using an inference engine and providing the one or more parameters to the synthesis engine, wherein the synthesis engine includes at least one of a placement, via assignment, routing, and metal pouring processes. The method may also include collecting process data from the placement, via assignment, routing, and metal pouring processes and training a machine learning system using the process data.