Scalable Server Regression Query Environment for Chip Design
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Solution Overview
Problem
Current chip design verification systems face challenges in providing adequate remote testing and analysis capabilities, leading to scalability issues due to large data generation and high memory consumption, which limits concurrent server requests and user interaction with regression farms.
Innovation Solution
A scalable server-implemented regression query environment is provided through an API executed by a processor, enabling remote testing and analysis of chip-design models via a server-side database manager, using REST architecture and Regression Query Language (RQL) to manage and analyze simulation test results, allowing dynamic attribute selection and user-defined queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of coverage data are generated from simulations, then chip design verification completeness is improved, but server scalability and concurrent request handling capability deteriorate due to high memory consumption and data analysis complexity
Solution Approach 1:
The system segments the analysis of large coverage datasets by implementing a query language (RQL) that allows users to retrieve only specific portions of data needed for their analysis, rather than processing the entire dataset. The database manager divides complex analysis tasks into manageable queries that can be executed efficiently, reducing server memory consumption while maintaining verification completeness.
Solution Approach 2:
The patent introduces a database manager as an intermediary component between the server and analysis queries. This mediator optimizes data retrieval operations, manages memory resources, and coordinates concurrent requests, enabling the server to handle multiple analysis tasks simultaneously without being overwhelmed by large datasets.
2Ease of operation
If comprehensive remote testing and analysis capabilities are provided, then user interaction with regression farms is improved, but server overload and scalability issues worsen due to heavy data processing requirements
Solution Approach 1:
The system enables users to perform self-service analysis by providing a query language interface that allows them to directly retrieve and analyze specific portions of test data without requiring server intervention for heavy processing. Users can formulate their own queries to extract needed information, reducing server processing burden while maintaining ease of operation.
Solution Approach 2:
The patent changes the operational parameters by introducing a query language that transforms complex data processing requests into optimized database queries. This allows the system to maintain comprehensive analysis capabilities while adjusting memory allocation and processing parameters to prevent server overload during concurrent operations.
Data Source
AI summary
A system and method for providing a scalable server-implemented regression query environment for remote testing and analysis of a chip-design model receives chip-design information, including the chip-design model to be tested and one or more attributes for testing the chip design model; receives a first regression simulation test request from the client-side integration client; initiates a proxy instance for a first regression simulation test to be executed by an application programming interface (API), based on the first regression simulation test request; selects, by the API, the attributes for testing the chip-design model; executes, by the API, the first regression simulation test on the chip-design model using the selected attributes; monitors, by a server-side database manager, the first regression simulation test during execution of the first regression simulation test; and stores, by the server-side database manager, one or more results of the first regression simulation test in a database.