TY - BOOK
T1 - Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
T2 - Use Cases and Emerging Challenges
AU - Pasricha, Sudeep
AU - Shafique, Muhammad
N1 - Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2023/1/1
Y1 - 2023/1/1
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.
AB - 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.
KW - Hardware-Aware neural architectural search
KW - Machine learning edge computing
KW - Machine learning embedded systems
KW - Machine learning IoT
KW - Smart cyber-physical systems
UR - http://www.scopus.com/inward/record.url?scp=85196249511&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196249511&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-40677-5
DO - 10.1007/978-3-031-40677-5
M3 - Book
AN - SCOPUS:85196249511
SN - 9783031406768
BT - Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
PB - Springer Nature
ER -