Neural Network Verification Parameter Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of coverage verification for computational logic is resource-intensive and time-consuming, requiring significant human labor and computational resources, with duplicative test cases and incomplete coverage being common issues in verifying complex designs.
Innovation Solution
A method involving the use of artificial neural networks (NNs) to reduce the complexity of test cases by identifying and focusing on relevant parameters and events, where NNs are trained based on test results to optimize the verification process, reducing the number of parameters and events, thereby decreasing the computational load and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of test cases are used to ensure adequate coverage, then verification completeness is improved, but resource consumption and time expenditure increase significantly
Solution Approach 1:
The patent creates a simplified copy (neural network model) of the complex verification system. This neural network is trained on a subset of test results and then used to predict outcomes for new test cases, replacing the need to execute all original complex simulations. The neural network serves as a lightweight replica that maintains verification effectiveness while dramatically reducing computational overhead and time requirements.
Solution Approach 2:
The patent performs preliminary actions by training the neural network in advance using a representative subset of test results. This pre-training phase allows the system to learn patterns and relationships beforehand, so that during actual verification, predictions can be made rapidly without re-running complex simulations. The preliminary training enables the system to achieve both completeness and efficiency.
2Reliability
If a large number of test cases are used to ensure adequate coverage, then verification completeness is improved, but resource consumption increases significantly
Solution Approach 1:
The patent creates a simplified copy (neural network model) of the complex verification system. This neural network is trained on a subset of test results and then used to predict outcomes for new test cases, replacing the need to execute all original complex simulations. The neural network serves as a lightweight replica that maintains verification effectiveness while dramatically reducing computational overhead and time requirements.
Solution Approach 2:
The patent transforms the verification approach by changing parameters from direct simulation execution to neural network prediction. The system identifies relevant parameters from test results and uses them as input features for the neural network. This parameter transformation allows the system to maintain verification quality while reducing computational resource requirements, as neural network inference is significantly less resource-intensive than full simulation execution.
3Reliability
If many test cases are designed to ensure adequate coverage, then verification thoroughness is improved, but test case management complexity increases
Solution Approach 1:
The patent creates a simplified copy (neural network model) of the complex verification system. This neural network is trained on a subset of test results and then used to predict outcomes for new test cases, replacing the need to execute all original complex simulations. The neural network serves as a lightweight replica that maintains verification effectiveness while dramatically reducing computational overhead and time requirements.
Solution Approach 2:
The patent implements feedback mechanisms where the neural network's predictions are continuously refined based on actual test results. The system learns from discrepancies between predicted and actual outcomes, adjusting its model to improve accuracy over time. This feedback loop enables the system to maintain thorough verification while managing complexity through adaptive learning rather than manual test case management.
4Measurement precision
If traditional simulation methods are used for verification, then accuracy is maintained, but processing speed decreases
Solution Approach 1:
The patent creates a simplified copy (neural network model) of the complex verification system. This neural network is trained on a subset of test results and then used to predict outcomes for new test cases, replacing the need to execute all original complex simulations. The neural network serves as a lightweight replica that maintains verification effectiveness while dramatically reducing computational overhead and time requirements.
Solution Approach 2:
The patent performs preliminary actions by training the neural network in advance using a representative subset of test results. This pre-training phase allows the system to learn patterns and relationships beforehand, so that during actual verification, predictions can be made rapidly without re-running complex simulations. The preliminary training enables the system to achieve both completeness and efficiency.
Data Source
AI summary
Techniques for analysis of verification parameters and reduction of training data are provided. A plurality of test results is received, where each of the plurality of test results specifies a respective one or more parameters and a respective one or more events. A list of parameters used to stimulate computing logic is determined. Additionally, a plurality of relevant parameters is generated, corresponding to parameters in the list of parameters that have at least two distinct values specified in the plurality of test results. A plurality of training cases is generated based on the plurality of test results and the plurality of relevant parameters. Further, a neural network is generated for design verification of the computing logic based on the plurality of relevant parameters. The neural network is trained based on the plurality of training cases.