Name: Convolutional Neural Network Accelerators: From Basic Design Principles to Advanced Security Applications (en Inglés)
SKU: 66895076
Price: 117.90 USD
Availability: InStock
Author: Halak, Basel
ISBN: 9783032085139

portada Convolutional Neural Network Accelerators: From Basic Design Principles to Advanced Security Applications (en Inglés)

Convolutional Neural Network Accelerators: From Basic Design Principles to Advanced Security Applications (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Halak, Basel

Idioma

Inglés

N° páginas

235

Encuadernación

Tapa Dura

ISBN13

9783032085139

Convolutional Neural Network Accelerators: From Basic Design Principles to Advanced Security Applications (en Inglés)

Halak, Basel (Autor) · Springer · Tapa Dura

Libro Nuevo Importado

Envío: 21 a 28 días háb.

$ 235.80$ 117.90

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 117.90

Llega entre el 01 Oct y el 15 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Convolutional Neural Network Accelerators: From Basic Design Principles to Advanced Security Applications (en Inglés)"

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.