paperback. Condizione: Good. Minimal markings from previous owner threwout pages.
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Speedyhen LLC, Hialeah, FL, U.S.A.
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
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Condizione: New. pp. 101.
Condizione: New.
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 120 pages. 9.25x6.10x0.28 inches. In Stock.
Lingua: Inglese
Editore: Springer Nature Switzerland, Springer, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 74,89
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
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Da: Brook Bookstore On Demand, Napoli, NA, Italia
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 120 pages. 9.25x6.10x0.28 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Jan 2020, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: 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.
Da: Majestic Books, Hounslow, Regno Unito
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 101.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 104,21
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 101.
Lingua: Inglese
Editore: Springer, Springer Jan 2020, 2020
ISBN 10: 3030357422 ISBN 13: 9783030357429
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 74,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.