Machine Learning Models for Electronic Design Performance Prediction
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Solution Overview
Problem
Conventional electronic design methods are inefficient and labor-intensive, requiring numerous manual iterations and simulations, especially due to the nonlinear and high-dimensional nature of analog design spaces, leading to increased design time and costs.
Innovation Solution
A method and system utilizing machine learning models to predict performance in electronic design, where a first model predicts performance data based on input parameters, and a second model generates new parameter values to achieve desired performance, with iterative training to optimize design efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual design methods are used, then design accuracy can be achieved through engineer expertise, but design time and costs increase significantly
Solution Approach 1:
The patent creates a virtual copy of the electronic design space using machine learning models. Instead of manually exploring the physical design space through repeated simulations, the system trains ML models on simulation data to create a predictive digital twin that can estimate performance metrics rapidly without requiring actual hardware or full-scale simulations for each design iteration.
Solution Approach 2:
The patent replaces the mechanical simulation-based design verification process with a machine learning-based prediction system. The ML models substitute for traditional circuit simulators, providing performance predictions without requiring the computationally intensive mechanical/electrical simulations that would otherwise be necessary for each design iteration.
2Reliability
If multiple iterations and simulations are performed manually, then design performance targets can be achieved, but the number of required simulations increases design complexity
Solution Approach 1:
The patent implements a self-service design optimization system where the machine learning models automatically perform design space exploration and parameter optimization without requiring manual intervention for each iteration. The system autonomously trains on simulation data, makes performance predictions, and guides design decisions, freeing engineers from repetitive manual simulation and analysis tasks.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the design parameters and performance outcomes. Instead of directly running full simulations for every design query, the ML models serve as a intermediate layer that provides rapid approximations, reducing the need for numerous complex simulations while maintaining acceptable accuracy for design exploration and optimization.
3Ease of operation
If sequential top-to-bottom design flow is followed, then design organization is maintained, but the nonlinear nature of analog design requires backtracking and iterative redesign
Solution Approach 1:
The patent transforms the static sequential design flow into a dynamic adaptive process. The machine learning models enable the design process to adaptively explore parameter spaces and identify optimal designs without being constrained by rigid sequential steps. The system can dynamically adjust design parameters and explore multiple design paths simultaneously, reducing the need for backtracking while maintaining organizational structure through systematic data management.
Data Source
AI summary
There is provided a method of predicting performance in electronic design based on machine learning using at least one processor, the method including: providing a first machine learning model configured to predict performance data for an electronic system based on a set of input design parameters for the electronic system; providing a second machine learning model configured to generate a new set of parameter values for the set of input design parameters for the electronic system based on a desired performance data provided for the electronic system; generating, using the second machine learning model, the new set of parameter values for the set of input design parameters for the electronic system based on the desired performance data provided for the electronic system; evaluating the set of input design parameters having the new set of parameter values for the electronic system to obtain an evaluated performance data associated with the set of input design parameters having the new set of parameter values; generating a new set of training data based on the set of input design parameters having the new set of parameter values and the evaluated performance data associated with the set of input design parameters having the new set of parameter values; and training the first machine learning model based on at least the new set of training data. There is also provided a corresponding system for predicting performance in electronic design based on machine learning.