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Record Nr. |
UNINA9910760273503321 |
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Titolo |
Embedded machine learning for cyber-physical, IoT, and edge computing : hardware architectures / / Sudeep Pasricha, Muhammad Shafique, editors |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2024] |
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© 2024 |
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ISBN |
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9783031195686 |
3-031-19568-X |
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Descrizione fisica |
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1 online resource (418 pages) |
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Disciplina |
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Soggetti |
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Embedded computer systems |
Cooperating objects (Computer systems) |
Artificial intelligence |
Embedded Systems |
Cyber-Physical Systems |
Artificial Intelligence |
Sistemes incrustats (Informàtica) |
Objectes cooperants (Sistemes informàtics) |
Informàtica a la perifèria |
Aprenentatge automàtic |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Efficient Hardware Acceleration for Embedded Machine Learning -- Memory Design and Optimization for Embedded Machine Learning -- Efficient Software Design of Embedded Machine Learning -- Hardware-Software Co-Design for Embedded Machine Learning -- Emerging Technologies for Embedded Machine Learning -- Mobile, IoT, and Edge Application Use-Cases for Embedded Machine Learning -- Cyber-Physical Application Use-Cases for Embedded Machine Learning. |
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Sommario/riassunto |
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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 |
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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. |
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