Convolutional Neural Network Accelerators (11 results)

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  • Language: English

    Published by Springer Nature Switzerland AG, Cham, 2026

    3032085136 / 9783032085139

    • Hardcover

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    Hardcover. Condition: new. Hardcover. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Language: English

    Published by Springer, 2025

    3032085136 / 9783032085139

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    Hardcover. Condition: Brand New. 200 pages. 9.25x6.10x9.49 inches. In Stock.

  • Language: English

    Published by Springer Nature Switzerland AG, Cham, 2026

    3032085136 / 9783032085139

    • Hardcover

    Seller: CitiRetail, Stevenage, United KingdomCitiRetail

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    Hardcover. Condition: new. Hardcover. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

  • Language: English

    Published by Springer Nature Switzerland AG, Cham, 2026

    3032085136 / 9783032085139

    • Hardcover

    Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller

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    Hardcover. Condition: new. Hardcover. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers. This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

  • Language: English

    Published by Springer, 2026

    3032085136 / 9783032085139

    • Hardcover

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers.

  • Language: English

    Published by Springer, 2026

    3032085136 / 9783032085139

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  • Language: English

    Published by Springer-Verlag Gmbh Apr 2026, 2026

    3032085136 / 9783032085139

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    Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers. 235 pp. Englisch.

  • Language: English

    Published by Springer Verlag GmbH, 2026

    3032085136 / 9783032085139

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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

  • Language: English

    Published by Springer-Verlag Gmbh Apr 2026, 2026

    3032085136 / 9783032085139

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    Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides comprehensive coverage of the state-of-the-art in Convolutional Neural Network (CNN) hardware accelerator design, security, and its applications in hardware security. The first part gives a foundational understanding of CNN architectures, emphasizing their computational demands and the necessity for specialized hardware solutions. It also proposes an emulation method with open-source code to mimic CNN hardware accelerator behavior. The second part presents security applications of CNN models, featuring a case study in Network-on-Chip security. It covers threat modeling, countermeasures, and the use of alternative machine learning models to CNNs. The third part explains security threats throughout the AI model production lifecycle, including software vulnerabilities and hardware risks, and explores techniques to enhance the robustness of CNN hardware accelerators, focusing on preventing hardware Trojan and backdoor attacks and analyzing the vulnerability levels of different CNN layers.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 235 pp. Englisch.

  • Language: English

    Published by Springer, 2026

    3032085136 / 9783032085139

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  • Language: English

    Published by Springer, 2026

    3032085136 / 9783032085139

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    Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios

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