ML Port Prediction for Electronic Design Files
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Solution Overview
Problem
Manual determination of ports in electronic systems design is time-consuming, laborious, and error-prone, especially when dealing with large schematic files containing thousands of nets, and existing systems rely heavily on human involvement, limiting the number of components and being inefficient.
Innovation Solution
A computer-implemented method using machine learning to process design files, converting them into a common format, extracting features, and applying a machine learning model to predict ports, which are then presented to users for editing and used to train improved models, supporting multiple design formats and reducing manual data creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of ports is used in electronic systems design, then engineers can accurately identify ports, but the process becomes time-consuming and laborious especially with large schematic files containing thousands of nets
Solution Approach 1:
The patent replaces the manual mechanical process of port identification with an automated machine learning system. The ML model processes schematic files and netlists to automatically predict ports, eliminating the need for engineers to manually examine thousands of nets while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The system enables self-service port identification where the machine learning model autonomously determines ports without human intervention. The model trains on historical data and independently processes new schematic files, providing automated port predictions that reduce engineer workload while maintaining consistency and accuracy.
2Reliability
If existing systems rely heavily on human involvement for port determination, then port identification can be accurate, but the number of components is limited and efficiency is reduced
Solution Approach 1:
The patent changes the fundamental parameter from manual inspection to automated machine learning prediction. By transforming the port identification process into an computational task with defined parameters and algorithms, the system achieves both high reliability through consistent application of trained models and improved productivity by processing larger numbers of components and schemas rapidly.
Solution Approach 2:
The machine learning system provides universal port identification capability that works across different types of electronic components and schematic formats. The single ML model can handle various component types, making the system universally applicable and significantly increasing productivity without sacrificing reliability through its ability to generalize from training data.
3Measurement precision
If manual data creation is used for ports, then data accuracy can be maintained, but the process is error-prone and time-consuming
Solution Approach 1:
The patent replaces error-prone manual data creation with automated machine learning prediction. The ML model consistently applies learned patterns from training data to predict ports, eliminating human errors such as typos, inconsistencies, and fatigue-related mistakes while maintaining high accuracy through algorithmic precision.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model learns from historical port data and continuously improves its predictions. By training on labeled examples and refining its algorithms based on performance metrics, the system reduces errors over time while maintaining high accuracy through iterative optimization and validation.
Data Source
AI summary
System and methods are provided for using machine learning to automatically process, extract, and categorize design engineering files. The system receives a design file and extracts features from the design file, which can include nets. The system trains a machine learning model to receive the features as input and output probabilities for multiple ports for the nets. The predicted ports are presented in a graphical user interface and are used to engineer electronic hardware systems.