Test Set Generation via Input Space Fracturing
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Solution Overview
Problem
Modern electronic design verification processes face inefficiencies due to the large input space of possible input combinations, making exhaustive testing computationally infeasible, particularly when constraints need to be satisfied, which limits the practicality of traditional iteration methods.
Innovation Solution
The method involves fracturing the input space into subspaces, solving each to determine if it contains input vectors that satisfy the constraints, and then searching only those subspaces with solutions for unique input vectors to generate a test set, using multiple solvers to efficiently identify solution-free regions and abort unnecessary searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive testing of all input combinations is performed, then verification completeness is improved, but computational feasibility deteriorates due to large input space
Solution Approach 1:
The input space is divided into multiple subspaces based on constraint conditions. Each subspace represents a portion of the input space that satisfies specific constraints. By segmenting the large input space into smaller manageable subspaces, the patent enables systematic exploration of constraint-satisfying regions without exhaustively testing the entire input space, thus resolving the contradiction between verification completeness and computational feasibility.
Solution Approach 2:
The patent extracts and removes portions of the input space that cannot satisfy the given constraints. By identifying and excluding these invalid regions, the method focuses computational resources only on subspaces that can potentially produce valid test vectors, thereby maintaining verification effectiveness while improving computational efficiency.
2Ease of operation
If traditional iteration methods are used to search input space, then simplicity of approach is improved, but practicality deteriorates due to large search space
Solution Approach 1:
The input space is segmented into subspaces defined by constraint conditions, transforming the simple but ineffective traditional iteration approach into a targeted search methodology. This segmentation enables practical application by limiting the search to relevant regions only.
Solution Approach 2:
Before performing the search, the patent performs preliminary actions to identify and exclude solution-free regions of the input space. This preliminary filtering action makes the subsequent search process practical by pre-eliminating impossible cases, while maintaining operational simplicity through automated constraint-based region identification.
Data Source
AI summary
Various embodiments provide for the determination of a test set that satisfies a coverage model, where portions of the search space need not be searched in order to generate the test set. With various embodiments, a search space defined by a set of inputs for an electronic design and a coverage model is identified. The search space is then fractured into subspaces. Subsequently, the subspaces are solved to determine if they include at least one input sequence that satisfies the coverage constraints defined in the coverage model. The subspaces found to include at least one input sequence that satisfies these coverage constraints, are then searched for unique input sequences in order to generate a test set. Subspaces found not to include at least one input sequence that satisfies the coverage constraints may be excluded from the overall search space.