MARC details
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| 001 - NÚMERO DE CONTROL |
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398123 |
| 003 - IDENTIFICADOR DEL NÚMERO DE CONTROL |
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ES-MaUEC |
| 005 - FECHA Y HORA DE LA ÚLTIMA TRANSACCIÓN |
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20240314174529.0 |
| 006 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA--CARACTERÍSTICAS DEL MATERIAL ADICIONAL |
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cr nn 008mamaa |
| 008 - DATOS DE LONGITUD FIJA--INFORMACIÓN GENERAL |
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230320s2023 sz | o |||| 0|eng d |
| 020 ## - NÚMERO INTERNACIONAL ESTÁNDAR DEL LIBRO |
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| Número Internacional Estándar del Libro |
9783031250996 |
| 024 7# - IDENTIFICADOR DE OTROS ESTÁNDARES |
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| Número estándar o código |
10.1007/978-3-031-25099-6 |
| Fuente del número o código |
doi |
| 040 ## - FUENTE DE LA CATALOGACIÓN |
|---|
| Centro catalogador/agencia de origen |
ES-MaUEC |
| Lengua de catalogación |
spa |
| Centro/agencia transcriptor |
ES-MaUEC |
| Centro/agencia modificador |
ES-MaUEC |
| 050 #4 - SIGNATURA TOPOGRÁFICA DE LA BIBLIOTECA DEL CONGRESO |
|---|
| Número de clasificación |
Q325.5 |
| Número de documento/Ítem |
2023 EB |
| 100 1# - ENTRADA PRINCIPAL--NOMBRE DE PERSONA |
|---|
| Nombre de persona |
Domingues, João L. C. P. |
| Término indicativo de función/relación |
autor |
| Código de función/relación |
http://id.loc.gov/vocabulary/relators/aut |
| 9 (RLIN) |
689502 |
| 245 10 - MENCIÓN DE TÍTULO |
|---|
| Título |
Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks |
| Mención de responsabilidad, etc. |
by João L C P Domingues, Pedro J C D C Vaz, António P L Gusmão, Nuno C G Horta, Nuno C C Lourenço, Ricardo M F Martins |
| 250 ## - MENCIÓN DE EDICIÓN |
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| Mención de edición |
1st ed 2023 |
| 264 #1 - PRODUCCIÓN, PUBLICACIÓN, DISTRIBUCIÓN, FABRICACIÓN Y COPYRIGHT |
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| Producción, publicación, distribución, fabricación y copyright |
Cham |
| Nombre del de productor, editor, distribuidor, fabricante |
Springer International Publishing |
| Fecha de producción, publicación, distribución, fabricación o copyright |
2023 |
| 300 ## - DESCRIPCIÓN FÍSICA |
|---|
| Extensión |
1 recurso en línea |
| 336 ## - TIPO DE CONTENIDO |
|---|
| Término de tipo de contenido |
texto |
| Código de tipo de contenido |
txt |
| Fuente |
rdacontent |
| 337 ## - TIPO DE MEDIO |
|---|
| Nombre/término del tipo de medio |
electrónico |
| Código del tipo de medio |
c |
| Fuente |
rdamedia |
| 338 ## - TIPO DE SOPORTE |
|---|
| Nombre/término del tipo de soporte |
recurso electrónico |
| Código del tipo de soporte |
cr |
| Fuente |
rdacarrier |
| 347 ## - CARACTERÍSTICAS DEL ARCHIVO DIGITAL |
|---|
| Tipo de archivo |
text file |
| Formato de codificación |
PDF |
| Fuente |
rda |
| 490 0# - MENCIÓN DE SERIE |
|---|
| Mención de serie |
SpringerBriefs in Computational Intelligence |
| Número Internacional Normalizado para Publicaciones Seriadas |
2625-3712 |
| 505 0# - NOTA DE CONTENIDO CON FORMATO |
|---|
| Nota de contenido con formato |
Chapter 1. Introduction -- Chapter 2. Related Work: Machine Learning and Electronic Design Automation -- Chapter 3. Convergence Classifier & Frequency Guess Predictor based on ANNs -- Chapter 4. Process, Voltage and Temperature Corner Performance Estimator using ANNs -- Chapter 5. Conclusions. |
| 520 ## - SUMARIO, ETC. |
|---|
| Sumario, etc. |
In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the sizing task of RF IC design, which is used in two different steps of the automatic design process. The advances in telecommunications, such as the 5th generation broadband or 5G for short, open doors to advances in areas such as health care, education, resource management, transportation, agriculture and many other areas. Consequently, there is high pressure in today's market for significant communication rates, extensive bandwidths and ultralow-power consumption. This is where radiofrequency (RF) integrated circuits (ICs) come in hand, playing a crucial role. This demand stresses out the problem which resides in the remarkable difficulty of RF IC design in deep nanometric integration technologies due to their high complexity and stringent performances. Given the economic pressure for high quality yet cheap electronics and challenging time-to-market constraints, there is an urgent need for electronic design automation (EDA) tools to increase the RF designers' productivity and improve the quality of resulting ICs. In the last years, the automatic sizing of RF IC blocks in deep nanometer technologies has moved toward process, voltage and temperature (PVT)-inclusive optimizations to ensure their robustness. Each sizing solution is exhaustively simulated in a set of PVT corners, thus pushing modern workstations' capabilities to their limits. Standard ANNs applications usually exploit the model's capability of describing a complex, harder to describe, relation between input and target data. For that purpose, ANNs are a mechanism to bypass the process of describing the complex underlying relations between data by feeding it a significant number of previously acquired input/output data pairs that the model attempts to copy. Here, and firstly, the ANNs disrupt from the most recent trials of replacing the simulator in the simulation-based sizing with a machine/deep learning model, by proposing two different ANNs, the first classifies the convergence of the circuit for nominal and PVT corners, and the second predicts the oscillating frequencies for each case. The convergence classifier (CCANN) and frequency guess predictor (FGPANN) are seamlessly integrated into the simulation-based sizing loop, accelerating the overall optimization process. Secondly, a PVT regressor that inputs the circuit's sizing and the nominal performances to estimate the PVT corner performances via multiple parallel artificial neural networks is proposed. Two control phases prevent the optimization process from being misled by inaccurate performance estimates. As such, this book details the optimal description of the input/output data relation that should be fulfilled. The developed description is mainly reflected in two of the system's characteristics, the shape of the input data and its incorporation in the sizing optimization loop. An optimal description of these components should be such that the model should produce output data that fulfills the desired relation for the given training data once fully trained. Additionally, the model should be capable of efficiently generalizing the acquired knowledge in newer examples, i.e., never-seen input circuit topologies. |
| 988 ## - NOTA LOCAL 598 |
|---|
| Nota local 598 (boletines) |
Springer_Computer_2023 |
| 650 #7 - PUNTO DE ACCESO ADICIONAL DE MATERIA--TÉRMINO DE MATERIA |
|---|
| Fuente del encabezamiento o término |
embne |
| 9 (RLIN) |
166090 |
| Término de materia o nombre geográfico como elemento de entrada |
Aprendizaje automático |
| 856 40 - LOCALIZACIÓN Y ACCESO ELECTRÓNICOS |
|---|
| Identificador Uniforme del Recurso |
https://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-25099-6 |
| Nota pública |
Acceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 999 ## - NÚMEROS DE CONTROL DE SISTEMA (KOHA) |
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| -- |
1 |
| 942 ## - ELEMENTOS DE PUNTO DE ACCESO ADICIONAL (KOHA) |
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| Fuente del sistema de clasificación o colocación |
Library of Congress Classification |
| Tipo de ítem Koha |
LIBRO-E NO PRÉSTAMO |
| 998 ## - DATOS ESTADÍSTICOS |
|---|
| Fecha de catalogación |
01/2024 |
| Tipo de materia |
E-book |
| Catalogador |
Irene Bastante |
| Catalogado |
Sí |