Parameter
- 426 Seiten
- 15 Lesestunden
Mehr zum Buch
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.
Buchkauf
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing, Sudeep Pasricha, Muhammad Shafique
- Sprache
- Erscheinungsdatum
- 2023
- product-detail.submit-box.info.binding
- (Hardcover)
Keiner hat bisher bewertet.
- Titel
- Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
- Untertitel
- Hardware Architectures
- Sprache
- Englisch
- Autor*innen
- Sudeep Pasricha, Muhammad Shafique
- Verlag
- Springer
- Erscheinungsdatum
- 2023
- Einband
- Hardcover
- Seitenzahl
- 426
- ISBN10
- 3031195671
- ISBN13
- 9783031195679
- Reihe
- Schlagwörter
- Sachbücher, Technologie & Industrie, Computer & Internet, Wissenschaft, Technologie, Elektronik & Elektrotechnik, Künstliche Intelligenz, Elektronik, Kybernetik, Maschinelles Lernen
- Beschreibung
- 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.