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constrained random simulation

Concept

Constrained random simulation is a hardware verification technique in which random input stimuli are generated subject to declaratively specified input constraints and then applied to validate design properties by simulation. Effectiveness depends critically on the performance of the constraint solver and the distribution of the generated solutions, particularly for mixed Boolean/integer variable domains.

First seen 7/17/2026
Last seen 7/28/2026
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WIKI

Constrained random simulation

Constrained random simulation is a technique used in hardware verification. It requires the random generation of input stimuli that obey a set of declaratively specified input constraints; the resulting stimuli are then applied in simulation to validate given design properties.[1]

The cited source describes constrained random simulation as the main workhorse of today's hardware verification flows.[1] In this workflow, two factors are highlighted as especially important to overall efficiency:[1]

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RELATIONSHIPS

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Hardware Verification ← uses 1e
Constrained random simulation is a primary technique used in hardware verification flows.
input constraints uses → 1e
Constrained random simulation relies on input constraints to generate valid stimuli.
Stimulus Generation uses → 1e
Constrained random simulation depends on stimulus generation to produce test inputs.
Input and Output Generation for the Verification of ALU: a Use Case ← mentions 75% 1e
The paper references constrained random simulation as a related concept in stimulus generation.

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Input and Output Generation for the Verification of ALU: a Use Case MENTIONS Extracted graph relationship

CITATIONS

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[1] Constrained random simulation is a main workhorse in hardware verification flows, requiring random generation of input stimuli that obey declaratively specified input constraints, which are then applied to validate design properties by simulation. Stimulus generation for constrained random simulation
[2] The effectiveness of constrained random simulation depends critically on (1) the performance of the constraint solver and (2) the distribution of the generated solutions. Stimulus generation for constrained random simulation
[3] Efficient constraint solving for stimulus generation addresses mixed Boolean/integer variable domains, and a hybrid solver based on Markov-chain Monte Carlo methods is proposed for good performance and distribution. Stimulus generation for constrained random simulation
[4] Random stimuli generation is currently a very important process for checking the correct behavior of various circuits, with stimuli ranging from binary values on simple circuit pins to a complex program in the data memory of a processor. Input and Output Generation for the Verification of ALU: a Use Case
[5] The current trend in stimulus generation focuses primarily on more complex circuits such as processors; simpler stimuli including test vectors can be generated directly in the simulation environment (e.g., Modelsim from Mentor Graphics) or via an external tool. Input and Output Generation for the Verification of ALU: a Use Case
[6] Specific stimuli generators exist for ASICs, DSPs, protocol interfaces, FPGA converters, and similar targets, but their use is limited to the particular system. Input and Output Generation for the Verification of ALU: a Use Case