SAT Solving

Technique

SAT solving is used as a constraint-solving backend in hardware-verification randomization flows. The provided evidence shows it appearing in PyVSC, CRAVE, and eUVM contexts: PyVSC relies completely on SAT solvers for multi-variable constraints, CRAVE exposes an integrated interface to BDD and SAT solvers, and eUVM delegates convoluted constraints beyond BDD handling to SMT/SAT solvers such as Z3, Boolector, and CMSGen.

First seen 5/25/2026

Last seen 6/3/2026

Evidence 3 chunks

Wiki v1

WIKI

Overview

SAT solving appears in the evidence as a backend technique for solving constraints in verification and constrained-random stimulus generation flows. In the cited RISCV-DV/eUVM discussion, SAT solvers are used alongside or after other constraint-solving approaches, especially when constraints are multi-variable or too convoluted for simpler approaches such as native or BDD-based solvers. [C1]

Role in constraint-solving stacks

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NEIGHBORHOOD

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RELATIONSHIPS

3 connections

PyVSC ← uses 100% 1e

PyVSC relies completely on SAT solvers for solving multi-variable constraints.

CRAVE ← uses 100% 1e

CRAVE provides an integrated interface to SAT solvers.

Code-based Test Generation for Validation of Functional Processor Descriptions ← compares with 80% 1e

The paper mentions comparing their constraint solver approach with SAT techniques as future work.

LINKED ENTITIES

3 links

PyVSC uses_sat_solving The evidence states that PyVSC does not deploy a BDD solver and relies completely on SAT solvers for solving multi-variable constraints.

CRAVE integrates_sat_solving The evidence states that CRAVE provides an integrated interface to a set of BDD and SAT solvers.

Z3 example_solver The evidence lists Z3 as one of the SMT/SAT solvers to which eUVM can delegate convoluted constraints.

CITATIONS

3 sources

3 citations — click to collapse

[1] C1: eUVM uses native solving for elementary single-domain constraints, BDD-based solving for medium-complexity multi-domain constraints, and SMT/SAT solvers such as Z3, Boolector, and CMSGen for convoluted constraints not handled by BDD. [PDF] Crafting a Million Instructions/Sec RISCV-DV - DVCon Proceedings

[2] C2: Pygen uses PyVSC for constraints; PyVSC lacks a BDD solver and relies completely on SAT solvers for multi-variable constraints, with the source reporting less-uniform solution spread and poor Pygen runtime performance relative to SV RISCV-DV. [PDF] Crafting a Million Instructions/Sec RISCV-DV - DVCon Proceedings

[3] C3: CRAVE provides an integrated interface to BDD and SAT solvers, but the source states it could not handle complex RISCV-DV constraints and that SystemC and CRAVE lacked multicore parallelism support. [PDF] Crafting a Million Instructions/Sec RISCV-DV - DVCon Proceedings