Constraint Solver Bit-Slice Rewriting for Backtracking Reduction
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Solution Overview
Problem
Constraint solvers face performance issues due to excessive backtracking and high memory usage when assigning random values to satisfy complex constraints, particularly with modulo and bit-slice constraints, leading to inefficient stimulus generation in constrained random simulation methodologies.
Innovation Solution
The system rewrites constraints to reduce backtracking by converting modulo constraints into standard logical and arithmetic operators and introduces auxiliary variables for bit-slice constraints, optimizing the representation of constraints using word-level circuits and binary decision diagrams to improve the efficiency of constraint solvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the constraint solver directly solves modulo and bit-slice constraints, then the constraints are satisfied, but excessive backtracking and high memory usage occur
Solution Approach 1:
The constraint solver segments modulo constraints into multiple bit-slice constraints by breaking down the modulo operation into individual bit comparisons. This segmentation transforms a single complex constraint into multiple simpler constraints that can be processed more efficiently, reducing backtracking while maintaining constraint satisfaction.
Solution Approach 2:
The patent introduces an intermediary representation layer between the original constraints and the solver engine. By converting modulo constraints into equivalent bit-slice constraints through an intermediate transformation process, the solver can work with a reformulated constraint set that reduces memory usage and backtracking without sacrificing correctness.
2Reliability
If the constraint solver uses detailed constraint representations, then constraint accuracy is maintained, but memory usage increases
Solution Approach 1:
The patent extracts the essential logical structure of modulo constraints and separates it from the full detailed representation. By taking out only the critical bit-level relationships needed for satisfaction and representing them as simplified bit-slice constraints, memory usage is reduced while maintaining the accuracy needed for correct constraint solving.
3Reliability
If the constraint solver processes complex constraints directly, then complete constraint logic is preserved, but backtracking increases
Solution Approach 1:
By segmenting complex modulo constraints into multiple bit-slice constraints, the solver can process each bit level independently and systematically. This segmentation preserves the complete constraint logic through systematic bit-level analysis while reducing backtracking because each segment can be solved more directly without requiring extensive backtracking through the entire constraint structure.
Data Source
AI summary
Methods and apparatuses are described for assigning random values to a set of random variables so that the assigned random values satisfy a set of constraints. A constraint solver can receive a set of constraints that is expected to cause performance problems when the system assigns random values to the set of random variables in a manner that satisfies the set of constraints. For example, modulo constraints and bit-slice constraints can cause the system to perform excessive backtracking when the system attempts to assign random values to the set of random variables in a manner that satisfies the set of constraints. The system can rewrite the set of constraints to obtain a new set of constraints that is expected to reduce and/or avoid the performance problems. The system can then assign random values to the set of random variables based on the new set of constraints.