| dc.contributor.author | Leal-Romo, Felipe J. | |
| dc.contributor.author | Chávez-Hurtado, José L. | |
| dc.contributor.author | Rayas-Sánchez, José E. | |
| dc.date.accessioned | 2019-08-30T20:23:01Z | |
| dc.date.accessioned | 2023-03-21T17:12:22Z | |
| dc.date.available | 2019-08-30T20:23:01Z | |
| dc.date.available | 2023-03-21T17:12:22Z | |
| dc.date.issued | 2018-12 | |
| dc.identifier.citation | F.J. Leal-Romo, J.L. Chávez-Hurtado, and J.E. Rayas-Sánchez, “Selecting surrogate-based modeling techniques for power integrity analysis,” in IEEE MTT-S Latin America Microwave Conf. (LAMC-2018), Arequipa, Peru, Dec. 2018, pp. 1-3. DOI: 10.1109/LAMC.2018.8699021 | es |
| dc.identifier.isbn | 978-1-5386-7334-8 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/73871 | |
| dc.description | In recent years, extensive usage of simulated power integrity (PI) models to predict the behavior of power delivery networks (PDN) on a chip has become more relevant. Predicting adequate performance against power consumption can yield to either cheap or costly design solutions. Since PI simulations including high-frequency effects are becoming more and more computationally complex and expensive, it is critical to develop reliable and fast models to understand system’s behavior to accelerate decision making during design stages. Hence, metamodeling techniques can help to overcome this challenge. In this work, a comparative study between different surrogate modeling techniques as applied to PI analysis is described. We model and analyze a PDN that includes two different power domains and a combination of remote sense resistors for communication and storage CPU applications. We aim at developing reliable and fast coarse models to make trade off decisions while complying with voltage levels and power consumption requirements. | es |
| dc.language.iso | eng | es |
| dc.publisher | IEEE | es |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf | es |
| dc.subject | DoE | es |
| dc.subject | Fitting Algorithms | es |
| dc.subject | Artificial Neural Networks (ANN) | es |
| dc.subject | Polynomial | es |
| dc.subject | Polynomial Surrogate Modeling | es |
| dc.subject | Power Delivery Network | es |
| dc.subject | Power Integrity | es |
| dc.subject | Support Vector Machines | es |
| dc.subject | Surrogate Models | es |
| dc.title | Selecting Surrogate-Based Modeling Techniques for Power Integrity Analysis | es |
| dc.type | info:eu-repo/semantics/article | es |
