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Condizione: New. pp. 400.

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Condizione: New.

Condizione: New. pp. 400.

Condizione: New. pp. 400.

Condizione: New.

Hardcover. Condizione: new. Hardcover. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condizione: new. Hardcover. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Hardcover. Condizione: Brand New. 400 pages. 9.00x6.00x0.75 inches. In Stock.

Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

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Buch. Condizione: Neu. Neuware - Accelerators for Convolutional Neural NetworksComprehensive and thorough resource exploring different types of convolutional neural networks and complementary acceleratorsAccelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration.The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators.Other sample topics covered in Accelerators for Convolutional Neural Networks include:\* How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systems\* State-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arrays\* iMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware acceleration\* Multi-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense accelerator\* Sparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN acceleratorsFor researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications.

Hardcover. Condizione: new. Hardcover. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems.Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation.Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies.Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research. This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Arslan MuhammadI am Environmental Scientist/Researcher at Government College University, Lahore-PakistanPakistan is the second-largest manufacturer of high-quality footballs worldwide. The current study was conducted to improve t.

Hardcover. Condizione: new. Hardcover. Accelerators for Convolutional Neural Networks Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators. Other sample topics covered in Accelerators for Convolutional Neural Networks include: How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systemsState-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arraysiMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware accelerationMulti-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense acceleratorSparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN accelerators For researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.