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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.
Introduction.- Efficient Hardware Acceleration for Embedded Machine Learning.- Memory Design and Optimization for Embedded Machine Learning.- Efficient Software Design of Embedded Machine Learning.- Hardware-Software Co-Design for Embedded Machine Learning.- Emerging Technologies for Embedded Machine Learning.- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning.- Cyber-Physical Application Use-Cases for Embedded Machine Learning.
Muhammad Shafique received his Ph.D. degree in computer science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful R&D activities across the globe. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, he is with the Division of Engineering at New York University Abu Dhabi (NYU-AD) in UAE, and is a Global Network faculty at the NYU’s Tandon School of Engineering (NYU-NY) in USA. He is the director of the eBrain research lab, and is also a Co-PI/Investigator in multiple large-scale research centers at NYUAD, including the Center of Artificial Intelligence and Robotics (CAIR), Center for Quantum and Topological Systems, Center of Cyber Security (CCS), and Center for InTeractIng urban nEtworkS (CITIES). Dr. Shafique has demonstrated success in leading team-projects, meeting deadlines for demonstrations, motivating team members to pea
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