This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Silicon photonics is beginning to play an important role in driving innovations in communication and computation for an increasing number of applications, from health care and biomedical sensors to autonomous driving, datacenter networking, and security. In recent years, there has been a significant amount of effort in industry and academia to innovate, design, develop, analyze, optimize, and fabricate systems employing silicon photonics, shaping the future of not only Datacom and telecom technology but also high-performance computing and emerging computing paradigms, such as optical computing and artificial intelligence. Different from existing books in this area, Silicon Photonics for High-Performance Computing and Beyond presents a comprehensive overview of the current state-of-the-art technology and research achievements in applying silicon photonics for communication and computation. It focuses on various design, development, and integration challenges, reviews the latest advances spanning materials, devices, circuits, systems, and applications. Technical topics discussed in the book include: • Requirements and the latest advances in high-performance computing systems • Device- and system-level challenges and latest improvements to deploy silicon photonics in computing systems • Novel design solutions and design automation techniques for silicon photonic integrated circuits • Novel materials, devices, and photonic integrated circuits on silicon • Emerging computing technologies and applications based on silicon photonics Silicon Photonics for High-Performance Computing and Beyond presents a compilation of 19 outstanding contributions from academic and industry pioneers in the field. The selected contributions present insightful discussions and innovative approaches to understand current and future bottlenecks in high-performance computing systems and traditional computing platforms, and the promise of silicon photonics to address those challenges. It is ideal for researchers and engineers working in the photonics, electrical, and computer engineering industries as well as academic researchers and graduate students (M.S. and Ph.D.) in computer science and engineering, electronic and electrical engineering, applied physics, photonics, and optics.

Cyber-Physical Systems: A Comprehensive Guide explores the complete sys-tem perspective, underlying theories, modelling, and the applications of Cyber Physical Systems (CPS). It aims to cover all topics ranging from discussion of ru-diments of the system, efficient management, to recent research challenges and issues. Editors aim to present the book in a self-sufficient manner and to achieve this, the book has been edited to include all the aspects of CPS. The book fo-cuses on the concept map of CPS including latest technological interventions; issues, challenges, and the integration of CPS with IoT & Big Data Analytics. This aims to bring together unique contributions on cyber-physical systems research and education with applications in industrial, agriculture, and medical domains. The main aim of the book is to provide a roadmap to the latest advancements to provide optimal solutions in the field of CPS. Features • Coverage of rudiments of the subject • Discussion of recent advancements in the associated field • Considers an audience of diverse domains • Suitable for students (both UG and PG level) and researchers in the field of CPS This book aims to present the emergence of Cyber Physical Systems in response to revolutionary advancements in IoT. While discussing the associated challenges, it also endeavors to devise efficient models which are competent to address these challenges. This book aims to cater to researchers and academicians working in the related field of CPS.

The rapid growth and reliance on cyber systems have permeated our society, government, and military which is demonstrated in this book. The authors discuss how AI-powered cyber systems are designed to protect against cyber threats and ensure the security and reliability of digital systems using artificial intelligence (AI) technologies. As AI becomes more integrated into various aspects of our lives, the need for reliable and trustworthy AI systems becomes increasingly important. This book is an introduction to all of the above-mentioned areas in the context of AI Embedded Assurance for Cyber Systems. This book has three themes. First, the AI/ML for digital forensics theme focuses on developing AI and ML powered forensic tools, techniques, software, and hardware. Second, the AI/ML for cyber physical system theme describes that AI/ML plays an enabling role to boost the development of cyber physical systems (CPS), especially in strengthening the security and privacy of CPS. Third, the AI/ML for cyber analysis theme focuses on using AI/ML to analyze tons of data in a timely manner and identify many complex threat patterns. This book is designed for undergraduates, graduate students in computer science and researchers in an interdisciplinary area of cyber forensics and AI embedded security applications. It is also useful for practitioners who would like to adopt AIs to solve cyber security problems.