Adaptive Planning Search Algorithm for Analog Circuit Verification

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Detalles Bibliográficos
Publicado en:	arXiv.org (Jun 23, 2023), p. n/a
Autor principal:	Manolache, Cristian
Otros Autores:	Andronache, Cristina, Caranica, Alexandru, Cucu, Horia, Buzo, Andi, Diaconu, Cristian, Pelz, Georg
Publicado:	Cornell University Library, arXiv.org
Materias:	Circuits Search algorithms Gaussian process Verification Integrated circuits Machine learning Analog circuits Specifications Adaptive algorithms
Acceso en línea:	Citation/Abstract Full text outside of ProQuest
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Descripción
Resumen:	Integrated circuit verification has gathered considerable interest in recent times. Since these circuits keep growing in complexity year by year, pre-Silicon (pre-SI) verification becomes ever more important, in order to ensure proper functionality. Thus, in order to reduce the time needed for manually verifying ICs, we propose a machine learning (ML) approach, which uses less simulations. This method relies on an initial evaluation set of operating condition configurations (OCCs), in order to train Gaussian process (GP) surrogate models. By using surrogate models, we can propose further, more difficult OCCs. Repeating this procedure for several iterations has shown better GP estimation of the circuit's responses, on both synthetic and real circuits, resulting in a better chance of finding the worst case, or even failures, for certain circuit responses. Thus, we show that the proposed approach is able to provide OCCs closer to the specifications for all circuits and identify a failure (specification violation) for one of the responses of a real circuit.
ISSN:	2331-8422
Fuente:	Engineering Database