Название: Frontiers of Quality Electronic Design (QED): AI, IoT and Hardware Security
Автор: Ali Iranmanesh
Издательство: Springer
Год: 2023
Страниц: 619
Язык: английский
Формат: pdf (true)
Размер: 23.6 MB

Quality Electronic Design (QED)’s landscape spans a vast region where territories of many participating disciplines and technologies overlap. This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.

Field programmable gate arrays (FPGAs) have gained popularity over the years and slowly made their way into advanced applications like machine learning, artificial intelligence, cloud services, military, and aerospace. Due to their flexibility in programming, FPGAs have become prevalent in system prototyping, hardware implementation for low-volume products, replacing obsolete components in legacy systems, and implementing hardware security modules.

Due to the increase in market share and features like field programmability, FPGAs have become a target for attackers. FPGAs are subjected to traditional security threats like Trojan insertion, side-channel analysis, reverse engineering, and information leakage through a covert channel. The majority of research efforts on FPGA security includes inserting hardware Trojans, reverse engineering intellectual property (IP) by decomposing or decrypting bitstream files, side-channel analysis attacks, and using counterfeit devices to degrade system performance. In existing literature the underlying FPGA CAD tool is considered trusted, and the investigation is performed typically on the stand-alone system.

Contents:
NAND Flash Memory Devices Security Enhancement Based on Physical Unclonable Functions
ReRAM-Based Neuromorphic Computing
Flash: A “Forgotten” Technology in VLSI Design
Nonvolatile Memory Technologies: Characteristics, Deployment, and Research Challenges
Data Analytics and Machine Learning for Coverage Closure
Cell-Aware Model Generation Using Machine Learning
Neuromorphic Computing: A Path to Artificial Intelligence Through Emulating Human Brains
AI for Cybersecurity in Distributed Automotive IoT Systems
Ultralow-Power Implementation of Neural Networks Using Inverter-Based Memristive Crossbars
AI-Based Hardware Security Methods for Internet-of-Things Applications
Enabling Edge Computing Using Emerging Memory Technologies: From Device to Architecture
IoT Commercial and Industrial Applications and AI-Powered IoT
Hardware and System Security: Attacks and Countermeasures Against Hardware Trojans
FPGA Security: Security Threats from Untrusted FPGA CAD Toolchain
DoS Attack Models and Mitigation Frameworks for NoC-Based SoCs
Defense against Security Threats with Regard to SoC Life Cycle
Defect Diagnosis Techniques for Silicon Customer Returns




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