Surrogate-based Analysis and Design Optimization of Power Delivery Networks for Cloud Computing Server Applications

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dc.contributor.advisorRayas-Sánchez, José E.
dc.contributor.authorLeal-Romo, Felipe
dc.date.accessioned2020-09-30T20:58:16Z
dc.date.available2020-09-30T20:58:16Z
dc.date.issued2020-09
dc.descriptionAs cloud computing escalates its usage model, it is very critical for vendors and companies that provide this service to reduce the total cost of ownership (TCO). By looking thoroughly into cost reduction opportunities, one of the most promising areas of opportunity is computer server’s power consumption. This means, the design of a robust hardware capable to perform adequately during high computing demand, avoiding large computer’s burning power translated as heat dissipation, and in consequence, more power consumed to keep datacenters cool. By taking this precedent as a baseline, power delivery (PD) engineers’ job is taking more relevance to provide reasonable recommendations that do not increase bill of materials (BOM) cost of motherboards and packages, and maximize performance avoiding excessive power consumption. In this doctoral dissertation, PD analysis is presented as a discipline that implies trade off decisions between performance and cost savings. It is demonstrated how the implementation of design of experiments (DoE) and space mapping (SM) can be very effective numerical approaches to aid PD engineers taking quicker and informed decisions, while optimizing the design of power delivery networks (PDN) for computer server’s design. To achieve a systematic optimization of the PDN, this PhD dissertation proposes a general methodology to implement surrogate-based models. First, by exploiting parameters extraction (PE) for lumped circuit PDN model to fit impedance profiles, and second by generating black box surrogate-based models to enable fast and accurate optimization of the PDN performance. For black box surrogate-based models’ generation, this dissertation compares four different machine learning techniques: Kriging, generalized regression neural networks (GRNN), polynomial surrogate models (PSM), and support vector machines (SVM). The best resultant metamodels are exploited for fast PDN optimization in two different industrial applications: a voltage regulator (VR) implementing dual power rail remote sensing, intended for communications and storage applications, to find the optimal sense resistors and loading conditions; and a multiphase VR of a server motherboard, by finding optimal compensation settings to decrease the number of bulk capacitors preventing CPU’s performance losses_MX
dc.identifier.citationLeal-Romo, F. (2020). Surrogate-based Analysis and Design Optimization of Power Delivery Networks for Cloud Computing Server Applications. Tesis de doctorado, Doctorado en Ciencias de la Ingeniería. Tlaquepaque, Jalisco: ITESO.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/6339
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes_MX
dc.subjectpower delivery networkses_MX
dc.subjectoptimization methodses_MX
dc.subjectsurrogate-based techniqueses_MX
dc.subjectcloud computing serverses_MX
dc.subjectcomputer designes_MX
dc.subjectvoltage regulatores_MX
dc.subjectimpedance profilees_MX
dc.subjectvoltage dropes_MX
dc.subjectdesign of experimentses_MX
dc.subjectKriginges_MX
dc.subjectSupport Vector Machinees_MX
dc.subjectParameters Extractiones_MX
dc.subjectDesign Optimizationes_MX
dc.subjectPolynomial Surrogate Modelses_MX
dc.titleSurrogate-based Analysis and Design Optimization of Power Delivery Networks for Cloud Computing Server Applicationses_MX
dc.typeinfo:eu-repo/semantics/doctoralThesises_MX
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_MX

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