Use Cases and Emerging Challenges Pasricha, Sudeep. editor. edt http://id.loc.gov/vocabulary/relators/edt Shafique, Muhammad. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XV, 571 p. 220 illus., 211 illus. in color. online resource. 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. edited by Sudeep Pasricha, Muhammad Shafique. Embedded computer systems Electronic circuits Cooperating objects (Computer systems) Embedded Systems Electronic Circuits and Systems Cyber-Physical Systems TK7895.E42 9783031406775 https://doi.org/10.1007/978-3-031-40677-5 https://doi.org/10.1007/978-3-031-40677-5 231006 20251030110821.0 978-3-031-40677-5