High Energy Efficiency Neural Network Processor With Combined Digital and Computing-in-memory Architecture - Couverture souple

Yue, Jinshan

9789819734795: High Energy Efficiency Neural Network Processor With Combined Digital and Computing-in-memory Architecture

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ISBN 10 : 9819734797 ISBN 13 : 9789819734795

Editeur : Springer Verlag, Singapore, 2025

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�diteur: Springer Verlag, Singapore
Date d'�dition: 2025
Langue: anglais
ISBN 10: 9819734797
ISBN 13: 9789819734795
Reliure: Broch�
Nombre de pages: 134
Coordonn�es du fabricant: ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Allemagne

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High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture

Jinshan Yue

Edit� par Springer, Springer Aug 2025, 2025

ISBN 10 : 9819734797 ISBN 13 : 9789819734795

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Neural network (NN) algorithms are driving the rapid development of modern artificial intelligence (AI). The energy-efficient NN processor has become an urgent requirement for the practical NN applications on widespread low-power AI devices. To address this challenge,this dissertation investigates pure-digital and digital computing-in-memory (digital-CIM) solutions and carries out four major studies.For pure-digital NN processors, this book analyses the insufficient data reuse in conventional architectures and proposes a kernel-optimized NN processor. This dissertation adopts a structural frequency-domain compression algorithm, named CirCNN. The fabricated processor shows 8.1x/4.2x area/energy efficiency compared to the state-of-the-art NN processor. For digital-CIM NN processors, this dissertation combines the flexibility of digital circuits with the high energy efficiency of CIM. The fabricated CIM processor validates the sparsity improvement of the CIM architecture for the first time. This dissertation further designs a processor that considers the weight updating problem on the CIM architecture for the first time.This dissertation demonstrates that the combination of digital and CIM circuits is a promising technical route for an energy-efficient NN processor, which can promote the large-scale application of low-power AI devices. 136 pp. Englisch. N� de r�f. du vendeur 9789819734795

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High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture

Jinshan Yue

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ISBN 10 : 9819734797 ISBN 13 : 9789819734795

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High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture (Springer Theses)

Yue, Jinshan

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ISBN 10 : 9819734797 ISBN 13 : 9789819734795

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High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture

Jinshan Yue

Edit� par Springer, Springer Aug 2025, 2025

ISBN 10 : 9819734797 ISBN 13 : 9789819734795

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Neural network (NN) algorithms are driving the rapid development of modern artificial intelligence (AI). The energy-efficient NN processor has become an urgent requirement for the practical NN applications on widespread low-power AI devices. To address this challenge, this dissertation investigates pure-digital and digital computing-in-memory (digital-CIM) solutions and carries out four major studies.For pure-digital NN processors, this book analyses the insufficient data reuse in conventional architectures and proposes a kernel-optimized NN processor. This dissertation adopts a structural frequency-domain compression algorithm, named CirCNN. The fabricated processor shows 8.1x/4.2x area/energy efficiency compared to the state-of-the-art NN processor. For digital-CIM NN processors, this dissertation combines the flexibility of digital circuits with the high energy efficiency of CIM. The fabricated CIM processor validates the sparsity improvement of the CIM architecture for the first time. This dissertation further designs a processor that considers the weight updating problem on the CIM architecture for the first time.This dissertation demonstrates that the combination of digital and CIM circuits is a promising technical route for an energy-efficient NN processor, which can promote the large-scale application of low-power AI devices.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 136 pp. Englisch. N� de r�f. du vendeur 9789819734795

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High Energy Efficiency Neural Network Processor with Combined Digital and Computing-in-Memory Architecture

Jinshan Yue

Edit� par Springer, Springer, 2025

ISBN 10 : 9819734797 ISBN 13 : 9789819734795

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural network (NN) algorithms are driving the rapid development of modern artificial intelligence (AI). The energy-efficient NN processor has become an urgent requirement for the practical NN applications on widespread low-power AI devices. To address this challenge,this dissertation investigates pure-digital and digital computing-in-memory (digital-CIM) solutions and carries out four major studies.For pure-digital NN processors, this book analyses the insufficient data reuse in conventional architectures and proposes a kernel-optimized NN processor. This dissertation adopts a structural frequency-domain compression algorithm, named CirCNN. The fabricated processor shows 8.1x/4.2x area/energy efficiency compared to the state-of-the-art NN processor. For digital-CIM NN processors, this dissertation combines the flexibility of digital circuits with the high energy efficiency of CIM. The fabricated CIM processor validates the sparsity improvement of the CIM architecture for the first time. This dissertation further designs a processor that considers the weight updating problem on the CIM architecture for the first time.This dissertation demonstrates that the combination of digital and CIM circuits is a promising technical route for an energy-efficient NN processor, which can promote the large-scale application of low-power AI devices. N� de r�f. du vendeur 9789819734795

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