Automation of Processor Verification Using Recurrent Neural Networks
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First seen 5/24/2026
Last seen 5/26/2026
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Automation of Processor Verification Using Recurrent Neural Networks
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This entity is identified as a paper titled “Automation of Processor Verification Using Recurrent Neural Networks.” No supporting evidence chunks or public context were provided, so no further technical claims about the paper can be made.
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13 connectionsThe paper mentions coverage state space when describing Codasip processors used for demonstration.
Marcela Zachariasova is listed as an author of the paper.
Pavel Smrz is listed as an author of the paper.
The paper proposes a new technique that dynamically alters constraints for PRG via a recurrent neural network.
The paper operates in the context of simulation-based verification of processors.
The paper generates stimuli using pseudorandom generators as part of its verification approach.
The paper employs stimulus generation to apply inputs to the processor under verification.
The paper monitors coverage to determine verification completeness, which is characteristic of coverage-driven verification.
The paper mentions programs loaded into program memory as another form of stimuli.
Martin Fajcik is listed as an author of the paper.
The paper mentions bit vectors as one form that stimuli can take when applied to processor input ports.
The paper reports that coverage closure is achieved much sooner with the proposed technique.
The paper mentions using isolated stimuli with high coverage for running regression tests.