Overview
- 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
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Table of contents (14 chapters)
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Efficient Hardware Acceleration for Embedded Machine Learning
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Memory Design and Optimization for Embedded Machine Learning
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Efficient Software Design for Embedded Machine Learning
Keywords
About this book
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.
Editors and Affiliations
About the editors
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 peak performance levels, and completion of independent challenging tasks. His experience is corroborated by strong technical knowledge and an educational record (throughout Gold Medalist). He also possesses an in-depth understanding of various video coding standards. His research interests are in brain-inspired computing, AI & machine learning hardware and system-level design, autonomous systems, wearable healthcare, energy-efficient systems, robust computing, hardware security, emerging technologies, FPGAs, MPSoCs, and embedded systems. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier venues. He has also organized many special sessions at flagship conferences (like DAC, ICCAD, DATE, IOLTS, and ESWeek), and has served as the Guest Editor for IEEE Design and Test Magazine (D&T), IEEE Transactions on Sustainable Computing (T-SUSC), IEEE Transactions on Embedded Computing (TECS), and Elsevier MICPRO. He has served as the TPC Chair of several conferences like IGSC, ISVLSI, PARMA-DITAM, RTML, ESTIMedia and LPDC; General Chair of ISVLSI, DDECS and ESTIMedia; Track Chair at DAC, ICCAD, DATE, IOLTS, DSD and FDL; and PhD Forum Chair of ISVLSI. He has also served on the program committees of numerous prestigious IEEE/ACM conferences including ICCAD, DAC, ISCA, DATE, CASES, ASPDAC, and FPL. He holds one US patent and has (co-)authored 6 Books, 15+ Book Chapters, 300+ papers in premier journals and conferences, and over 50 archive articles. Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, the AI-2000 Chip Technology Most Influential Scholar Award in 2020, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences like CODES+ISSS, DATE, DAC and ICCAD, Best Master Thesis Award, DAC'14 Designer Track Best Poster Award, IEEE Transactions of Computer "Feature Paper of the Month" Awards, and Best Lecturer Award. Dr. Shafique is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a professional member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC.
Bibliographic Information
Book Title: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
Book Subtitle: Hardware Architectures
Editors: Sudeep Pasricha, Muhammad Shafique
DOI: https://doi.org/10.1007/978-3-031-19568-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-19567-9Published: 02 October 2023
Softcover ISBN: 978-3-031-19570-9Due: 01 November 2023
eBook ISBN: 978-3-031-19568-6Published: 30 September 2023
Edition Number: 1
Number of Pages: XIV, 412
Number of Illustrations: 291 b/w illustrations, 165 illustrations in colour
Topics: Circuits and Systems, Cyber-physical systems, IoT, Artificial Intelligence