03041nam a22005175i 4500 978-3-031-40677-5 DE-He213 20251030110821.0 cr nn 008mamaa 231006s2024 sz | s |||| 0|eng d 9783031406775 978-3-031-40677-5 10.1007/978-3-031-40677-5 doi TK7895.E42 UKM bicssc COM092000 bisacsh UKM thema Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing [electronic resource] : Use Cases and Emerging Challenges / edited by Sudeep Pasricha, Muhammad Shafique. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 571 p. 220 illus., 211 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Embedded computer systems. 64625 Electronic circuits. Cooperating objects (Computer systems). 64303 Embedded Systems. Electronic Circuits and Systems. Cyber-Physical Systems. 65638 Pasricha, Sudeep. editor. edt http://id.loc.gov/vocabulary/relators/edt 66329 Shafique, Muhammad. editor. edt http://id.loc.gov/vocabulary/relators/edt 66330 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031406768 Printed edition: 9783031406782 Printed edition: 9783031406799 https://doi.org/10.1007/978-3-031-40677-5 ZDB-2-ENG ZDB-2-SXE 56376 56376