Machine Learning Model Predicting EDA Tool Outputs
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Solution Overview
Problem
The circuit design process is hindered by the need for repeated iterations through computationally intensive stages of electronic design automation (EDA) tools, which are time-consuming and inefficient, especially when testing design parameters, as current methods require hours or days to generate feedback, leading to wasted time and resources.
Innovation Solution
Employing machine learning models to predict EDA tool outputs without executing the EDA tool, allowing design engineers to quickly assess potential results and make informed decisions on experimental runs, thereby reducing the need for lengthy simulations and optimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EDA tools are executed to generate design feedback, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates a machine learning model that copies the behavior and output patterns of the EDA tool without executing the actual EDA tool. The model is trained on historical EDA tool inputs and outputs, learning to replicate the tool's decision-making process. This allows rapid prediction of EDA tool outputs while maintaining reasonable accuracy, eliminating the need for time-consuming full EDA tool executions during iterative design exploration.
Solution Approach 2:
The machine learning model is trained in advance on comprehensive datasets containing various design scenarios and corresponding EDA tool outputs. This preliminary training phase captures the complex relationships and design rules that the EDA tool would otherwise require hours or days to analyze. During actual design iterations, the pre-trained model can quickly predict outcomes without needing to re-execute the full EDA tool analysis.
2Manufacturing precision
If EDA tools are executed to test design parameters, then manufacturing precision is improved, but productivity deteriorates
Solution Approach 1:
The machine learning model replicates the EDA tool's optimization capabilities by learning from training data that includes various design parameters and their optimal configurations. The model captures the complex design rules and constraints that the EDA tool uses to achieve manufacturing precision, enabling rapid parameter testing without full EDA tool execution.
Solution Approach 2:
The system enables rapid exploration of different design parameters by using the machine learning model to predict outcomes for various parameter configurations. Designers can quickly adjust parameters and receive immediate feedback from the model, allowing extensive parameter optimization that would be prohibitively slow using traditional EDA tool execution for each parameter set.
3Measurement precision
If computational resources are allocated to EDA tool execution, then measurement precision is improved, but loss of energy and productivity worsen
Solution Approach 1:
Instead of executing the computationally intensive EDA tool, the system uses a machine learning model that has been trained to copy the EDA tool's output patterns. The model requires minimal computational resources during inference compared to the full EDA tool execution, significantly reducing energy consumption while maintaining acceptable prediction accuracy for design decisions.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to predict outputs of electronic design automation (EDA) tools using machine learning are disclosed. An example apparatus includes memory; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to: access circuit design data to be optimized by an EDA tool as part of a circuit design process for an integrated circuit; extract features from the circuit design data; apply a machine learning model to the features to estimate an output of the EDA tool, the estimated output determined without execution of the EDA tool; and provide results of the estimated output.