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Publicado por Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Idioma: Inglés
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 74,89
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices' sizes to circuits' performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices' sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's performances as input features and devices' sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: California Books, Miami, FL, Estados Unidos de America
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Publicado por Springer International Publishing, Springer Nature Switzerland Jan 2020, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 74,89
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices¿ sizes to circuits¿ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices¿ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit¿s performances as input features and devices¿ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 108,36
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Añadir al carritoPaperback. Condición: Brand New. 120 pages. 9.25x6.10x0.28 inches. In Stock.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Publicado por Springer International Publishing, Springer International Publishing Jan 2020, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 74,89
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices' sizes to circuits' performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices' sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's performances as input features and devices' sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies. 120 pp. Englisch.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 80,39
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Añadir al carritoPaperback. Condición: Brand New. 120 pages. 9.25x6.10x0.28 inches. In Stock. This item is printed on demand.
Librería: Majestic Books, Hounslow, Reino Unido
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Añadir al carritoCondición: New. Print on Demand pp. 101.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 101.