pseudorandom generator (PRG)

Technique

In the provided evidence, pseudorandom generators (PRGs) are used in simulation-based processor verification to generate stimuli that are applied to processor inputs while functional coverage is monitored. The cited work proposes dynamically altering PRG constraints with a recurrent neural network using coverage feedback, reporting faster coverage closure and extraction of a small high-coverage stimulus set for regression tests.

First seen 5/26/2026

Last seen 5/26/2026

Evidence 2 chunks

Wiki v1

WIKI

Overview

A pseudorandom generator (PRG) is discussed in the supplied evidence as a stimulus-generation component in simulation-based verification of processors. The verification flow described generates stimuli using PRGs, applies those stimuli to processor inputs, and monitors achieved functional coverage to judge verification completeness. [C1]

Stimulus forms

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NEIGHBORHOOD

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RELATIONSHIPS

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Automation of Processor Verification Using Recurrent Neural Networks ← uses 100% 2e

The paper generates stimuli using pseudorandom generators as part of its verification approach.

LINKED ENTITIES

1 links

Coverage Feedback input_to_constraint_adaptation The recurrent neural network receives coverage feedback from simulation of the design under verification to alter PRG constraints.

CITATIONS

5 sources

5 citations — click to expand

[1] C1: In simulation-based verification of processors, stimuli are generated using PRGs, applied to processor inputs, and functional coverage is monitored to determine verification completeness. Automation of Processor Verification Using Recurrent Neural Networks

[2] C2: Stimuli can be represented as bit vectors applied to processor input ports or as programs loaded directly into program memory. Automation of Processor Verification Using Recurrent Neural Networks

[3] C3: The paper proposes dynamically altering PRG constraints via a recurrent neural network that receives coverage feedback from simulation of the design under verification. Automation of Processor Verification Using Recurrent Neural Networks

[4] C4: The demonstration used processors provided by Codasip, with coverage state spaces described as reasonably large and different across processor kinds; the authors state that the techniques are widely applicable. Automation of Processor Verification Using Recurrent Neural Networks

[5] C5: The reported experiments show faster coverage closure and the ability to isolate a small set of high-coverage stimuli for regression tests. Automation of Processor Verification Using Recurrent Neural Networks