embedded machine learning cyber physical (53 résultats)

Etat : New.

Etat : New.

Etat : New.

Etat : New. 2024th edition NO-PA16APR2015-KAP.

Etat : New. In.

Taschenbuch. Etat : Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Hardware Architectures | Muhammad Shafique (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer Nature Switzerland | EAN 9783031195709 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 428 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Etat : New.

Etat : New.

Etat : Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 428 pp. Englisch.

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Hardcover. Etat : Brand New. 426 pages. 9.25x6.10x9.21 inches. In Stock.

Etat : New.

Etat : New. 2024th edition NO-PA16APR2015-KAP.

Taschenbuch. Etat : Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Software Optimizations and Hardware/Software Codesign | Sudeep Pasricha (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9783031399343 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Etat : Neu. Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing | Use Cases and Emerging Challenges | Sudeep Pasricha (u. a.) | Taschenbuch | xv | Englisch | 2024 | Springer | EAN 9783031406799 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Etat : New. In.

Etat : New. In.

Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 492 pp. Englisch.

Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 588 pp. Englisch.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards thegoal ofenabling efficient implementation ofmachine learning models onresource-constrained systems, covering different application domains. Thefocus is onpresenting interesting and new use cases ofapplying machine learning toinnovative application domains, exploring theefficient hardware design ofefficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques forenergy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques forachieving even greater energy, reliability, and performance benefits.Discusses efficient implementation ofmachine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage ofhardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit frommachine learning.

Etat : New. pp. 588 1st ed. 2024 edition NO-PA16APR2015-KAP.

Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 492 pp. Englisch.

Buch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 588 pp. Englisch.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 todemonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances towards thegoal ofenabling efficient implementation ofmachine learning models onresource-constrained systems, covering different application domains. Thefocus is onpresenting interesting and new use cases ofapplying machine learning toinnovative application domains, exploring theefficient hardware design ofefficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques forenergy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques forachieving even greater energy, reliability, and performance benefits.Discusses efficient implementation ofmachine learning in embedded, CPS, IoT, and edge computing;Offers comprehensive coverage ofhardware design, software design, and hardware/software co-design and co-optimization;Describes real applications todemonstrate how embedded, CPS, IoT, and edge applications benefit frommachine learning.