Convolutional Neural Network Accelerators : From Basic Design Principles to Advanced Security Applications

Halak, Basel (EDT)

Springer Nature Switzerland AG（2025/12発売）

• 製本 Hardcover:ハードカバー版
• 言語 ENG
• 商品コード 9783032085139

Full Description

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.

Contents

Introduction.- Hardware implementation of CNN hardware accelerator.- A tutorial on the design and hardware implementation of a CNN-based machine learning model.- Performance Optimization of CNN Hardware.- Security Optimization of a CNN-based machine learning model.- Application of CNN Accelerators in Hardware Security - Security Issues in Networks-on-Chip.- Application of CNN Accelerators in Hardware Security - Countermeasures for Security Issues in Networks-on-Chip.- Application of CNN Accelerators in Hardware Security - Other Machine Learning Models for Networks-on-Chip Security.- Conclusions and Future Opportunities.