machine learning embedded computing (15 Ergebnisse)

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 428 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: 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.

Buch. Zustand: 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.

Zustand: New. In.

Taschenbuch. Zustand: 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. Zustand: 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.

Zustand: New. KlappentextrnrnThis book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communicatio.

Zustand: New. In.

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

Zustand: New. In.

Zustand: New. In.

Buch. Zustand: 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. Zustand: 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.

Hardcover. Zustand: Brand New. 491 pages. 9.25x6.10x1.30 inches. In Stock.

Hardcover. Zustand: Brand New. 586 pages. 9.25x6.10x9.21 inches. In Stock.