Machine Learning Matrix Solver for EDA Parameter Tuning
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Solution Overview
Problem
In Electronic Design Automation (EDA), matrix solving operations require manual tuning of parameters like matrix partitioning, pivoting, and convergence tolerance, which is time-consuming and inefficient, especially when dealing with new circuitry, operating points, or process nodes, as existing hardcoded knowledge may not be sufficient.
Innovation Solution
A computer-implemented method and system that trains and generates a matrix solving Machine Learning model using selected data to optimize parameters such as matrix partitioning, pivoting, preconditioner, and degeneracy avoidance, allowing for automated optimization and adaptation to specific domains and problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of matrix solving parameters is used, then domain knowledge can be applied, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with domain knowledge and matrix solving expertise before actual use. The models are trained offline on historical data and expert knowledge, storing optimized parameter configurations and solution strategies in advance. During runtime, the pre-trained models rapidly infer optimal parameters without requiring manual tuning, thus resolving the contradiction between solution quality and time consumption.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between domain knowledge and matrix solving operations. The ML models act as mediators that encode expert knowledge, historical patterns, and optimization strategies, translating complex domain expertise into automated parameter recommendations. This intermediary layer eliminates the need for direct manual tuning while preserving the benefits of domain knowledge.
2Reliability
If hardcoded domain knowledge is used, then existing problems can be solved, but adaptability to new requirements decreases
Solution Approach 1:
The system implements dynamics by transitioning from static hardcoded knowledge to dynamic machine learning models that can adapt and learn. The ML models are designed to be retrainable and updatable, allowing them to incorporate new domain knowledge, handle emerging circuitry types, and adjust to changing requirements. This dynamic characteristic enables the system to maintain reliability for existing problems while gaining adaptability to new scenarios.
Solution Approach 2:
The patent applies parameter changes by using machine learning models that can adjust solving parameters dynamically based on input characteristics. Instead of fixed hardcoded parameters, the system uses ML-inferred parameters that adapt to different problem types, circuitry configurations, and operating conditions. This allows the system to maintain reliable solutions for known problems while adapting to new requirements through parameter optimization.
3Manufacturing precision
If extensive regression and debugging is performed, then parameter optimization improves, but productivity decreases
Solution Approach 1:
The system implements self-service by enabling machine learning models to automatically optimize parameters without requiring extensive manual regression and debugging. The ML models self-learning from training data, automatically identifying optimal parameter configurations through pattern recognition and inference. This automation eliminates the need for time-consuming manual optimization cycles, thereby improving productivity while maintaining parameter optimization quality.
Solution Approach 2:
The patent incorporates feedback mechanisms where machine learning models learn from historical solving results, performance metrics, and outcome data. The system continuously refines its parameter recommendations based on feedback from actual matrix solving operations, improving optimization accuracy over time without requiring manual intervention. This automated feedback loop replaces extensive regression and debugging while enhancing both productivity and parameter optimization.
Data Source
AI summary
A method trains and generates a matrix solving approach library package for optimizing a matrix solving application. A computer system with implemented the method may 1) receive requests to train a matrix solving Machine Learning (ML) model; 2) design a model structure of the ML Model accordingly; 3) select a set of matrices solving sampling data for training the defined matrix solving ML model; 4) use the selected matrix solving data and constructed IMSA Structure as inputs to train the matrix solving ML model; 5) generates a new matrix solving ML model with optimized IMSA parameters as ML model outputs; optimize the weights for each ML model node according to the provided training data sets; 6) verify the trained matrix solving approach library package with untrained data sets (matrix solving problems). The trained matrix solving approach library package may optimize matric solving application for solving matrix with result.