GAI-Based Test Generation Framework for Processor Verification
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Solution Overview
Problem
Conventional processor verification methods face limitations such as limited diversity in test generation, potential blind spots, limited automation, and time-consuming manual test suite creation, especially for microprocessors with complex instruction sets, and lack processor-independent test suite generation utilities for RTL design verification and FPGA validation.
Innovation Solution
A generative artificial intelligence (GAI)-based random test generation framework (GRVP) that utilizes retrieval augmented generation (RAG) and conventional methods to create intelligent, automated test sequences, incorporating GAI-assisted random verification programs (GRVP) and processors-specific APIs to generate diverse and self-checking test cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional random test generators are used, then automation is achieved, but test diversity and verification coverage are limited
Solution Approach 1:
The patent introduces GAI models as an intermediary between conventional test generators and the test generation process. The GAI model enhances automated test generation by providing intelligent scenario selection and realistic test case generation, thereby improving verification coverage while maintaining automation. The GAI model acts as a mediator that bridges the gap between automated generation and comprehensive coverage.
Solution Approach 2:
The patent combines multiple approaches into a composite test generation system that integrates conventional random test generators with GAI-based intelligent generation. This composite approach leverages the automation of conventional methods while adding the coverage enhancement of GAI, creating a hybrid system that achieves both automation and comprehensive verification coverage.
2Quantity of substance
If completely random test cases are generated, then test diversity increases, but realistic scenario coverage decreases
Solution Approach 1:
The patent applies local quality by making different parts of the test generation process have different characteristics. Conventional random generators provide diversity in the overall test suite, while GAI-based generation focuses on creating realistic scenarios in specific critical areas. This localized approach ensures both diversity and realism in appropriate contexts.
Solution Approach 2:
The patent changes the parameters of test generation by transitioning from purely random parameter selection to GAI-guided parameter selection. The GAI model adjusts test case parameters based on learned patterns from realistic scenarios, thereby maintaining diversity while ensuring realistic coverage without requiring complete redesign of the generation process.
3Measurement precision
If processor-specific test suites are used, then verification accuracy improves, but adaptability to different processors decreases
Solution Approach 1:
The patent implements universality by designing a GAI-based test generation framework that can adapt to multiple processor architectures. The system uses processor-independent instruction set specifications as input and generates appropriate test cases for different target processors. This allows a single universal system to provide accurate verification across multiple processor types without requiring processor-specific customization.
Solution Approach 2:
The patent introduces dynamics by making the test generation system adaptable and configurable for different processor architectures. The GAI model can be trained on or configured for specific instruction set architectures, allowing the system to dynamically adjust its test generation behavior based on the target processor while maintaining a unified framework. This dynamic adaptability enables both accuracy for specific processors and versatility across different processors.
Data Source
AI summary
Techniques pertaining to generative artificial intelligence (GAI)-based random test generation framework for processor verification are described. An apparatus generates one of more random test cases with an aid of a GAI-assisted random verification program (GRVP) framework. The apparatus also performs validation testing on a design of a processor using the one or more random test cases.