TY  - BOOK
AU  - Pasricha,Sudeep
AU  - Shafique,Muhammad
ED  - SpringerLink (Online service)
TI  - Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges 
SN  - 9783031406775
AV  - TK7895.E42 
U1  - 006.22 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Embedded computer systems
KW  - Electronic circuits
KW  - Cooperating objects (Computer systems)
KW  - Embedded Systems
KW  - Electronic Circuits and Systems
KW  - Cyber-Physical Systems
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-40677-5
ER  -