Equalization Tuning of the PCIe Physical Layer by Using Machine Learning in Industrial Post-silicon Validation

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dc.contributor.authorRangel-Patiño, Francisco E.
dc.contributor.authorViveros-Wacher, Andres
dc.contributor.authorRajyaguru, Chintan
dc.contributor.authorVega-Ochoa, Edgar A.
dc.contributor.authorRodriguez-Saenz, Sofia D.
dc.contributor.authorSilva-Cortes, Johana L.
dc.contributor.authorShival, Hemanth
dc.contributor.authorRayas-Sánchez, José E.
dc.date.accessioned2023-08-14T22:59:24Z
dc.date.available2023-08-14T22:59:24Z
dc.date.issued2023-06-14
dc.descriptionThe increasing complexity of high-speed computer platforms has made post-silicon validation a highly demanding industrial task. A large portion of the circuits to be validated in modern microprocessors corresponds to high-speed input/output (HSIO) links, imposing the need to efficiently tune the transmitter (Tx) and receiver (Rx) equalizers. In this work, we first use unsupervised machine learning techniques to cluster all available post-silicon data from different channels, dividing them into distinct sets of channel conditions. We then develop statistical supervised machine learning models, based on Gaussian process regression (GPR), to predict the eye diagram margins within each data subset. We finally optimize the GPR-based models to obtain the optimal tuning settings for the specific channels. Our proposed method is validated by measurements of the functional eye diagram of an actual industrial computer platform.es_MX
dc.description.sponsorshipITESO, A.C.es
dc.identifier.citationF. E. Rangel-Patiño, A. Viveros-Wacher, C. Rajyaguru, E. A. Vega-Ochoa, S. D. Rodriguez-Saenz, J. L. Silva-Cortes, H. Shival, and J. E. Rayas-Sánchez, “Equalization tuning of the PCIe physical layer by using machine learning in industrial post-silicon validation,” in IEEE MTT-S Int. Microwave Symp. Dig., San Diego, CA, Jun. 2023, pp. 628.es_MX
dc.identifier.isbn978-1-6654-9614-8
dc.identifier.issn2575-4742
dc.identifier.urihttps://hdl.handle.net/11117/9647
dc.language.isoenges_MX
dc.publisherIEEEes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdfes_MX
dc.subjectPCIe, equalization, post-silicon validation, machine learning, clustering, Gaussian process regressiones_MX
dc.subjectPCIees_MX
dc.subjectEqualizationes_MX
dc.subjectPost-silicon Validationes_MX
dc.subjectMachine Learninges_MX
dc.subjectClusteringes_MX
dc.subjectGaussian Process Regressiones_MX
dc.titleEqualization Tuning of the PCIe Physical Layer by Using Machine Learning in Industrial Post-silicon Validationes_MX
dc.typeinfo:eu-repo/semantics/conferencePaperes_MX
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_MX

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