Object-Oriented Constraint Partitioning

Technique

Object-Oriented Constraint Partitioning is a constrained-random verification technique that splits a large single-class constraint problem into a base class plus opcode-category subclasses. In the AMD/Synopsys microcode stimulus generator example, this hierarchy reduced memory requirements and improved performance while preserving weighted control over generated instruction distributions.

First seen 5/25/2026

Last seen 5/26/2026

Evidence 2 chunks

Wiki v1

WIKI

Overview

Object-Oriented Constraint Partitioning is a technique for structuring constrained-random generators by moving global constraints into a base class and placing category-specific constraints into derived subclasses. In the AMD/Synopsys microcode stimulus generator example, the original generator used one opcode class containing constraints for all opcodes; the later object-oriented version used a base instruction class plus child classes for groups of related opcodes. This hierarchical partitioning reduced the size of each randomization problem, drastically reduced memory requirements, and increased performance.

Motivation

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RELATIONSHIPS

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Hierarchical Constrained-Random Test Generation ← implements 90% 1e

The hierarchical approach implements object-oriented constraint partitioning to reduce memory and improve performance.

Base Instruction Class uses → 90% 1e

Object-oriented constraint partitioning uses a base instruction class to hold common constraints and data members.

LINKED ENTITIES

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Synopsys VCS Constraint Solver used_with The evidence states that the AMD/Synopsys generator used the Synopsys VCS constraint solver and describes VCS constraint profiling and solver partitioning.

CITATIONS

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[1] The technique uses an object-oriented hierarchy with a base class for global opcode constraints and subclasses for groups of related opcodes. Generating AMD microcode stimuli using VCS constraint solver

[2] Hierarchical partitioning of constraints into smaller opcode groups reduced memory requirements and increased performance. Generating AMD microcode stimuli using VCS constraint solver

[3] The original single-class opcode generator had approximately 100 random variables and 800 constraint equations, making the solver problem large and potentially slow. Generating AMD microcode stimuli using VCS constraint solver

[4] The generator architecture used an upper SystemVerilog random sequence layer with weighted knobs and a lower opcode class layer randomized with constraints and weights. Generating AMD microcode stimuli using VCS constraint solver

[5] In the multi-class design, opcode categories mapped to test-interface knobs or weights, the base class held common data and methods, and each child class held opcode-category-specific constraints. Generating AMD microcode stimuli using VCS constraint solver

[6] When tests directly control lower-level subclass items, the evidence recommends making subclass-selection decisions first, potentially by randomizing a wrapper class before allocating and randomizing the subclass. Generating AMD microcode stimuli using VCS constraint solver

[7] The VCS constraint profiler reports runtime performance by cumulative randomize calls, per-randomize call, and per partition, and VCS can partition unrelated random variables within a randomize call for independent solving. Generating AMD microcode stimuli using VCS constraint solver