Описание: Presenting a mathematical model for on-chip routers which can be used for NoC performance analysis, this book reflects the shift from computation- to communication-based design that has resulted from the increasing complexity of so-called `systems-on-chip`.
Автор: Moons, Bert Bankman, Daniel Verhelst, Marian Название: Embedded deep learning ISBN: 3319992228 ISBN-13(EAN): 9783319992228 Издательство: Springer Рейтинг: Цена: 10976.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning.
Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices;
Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy - applications, algorithms, hardware architectures, and circuits - supported by real silicon prototypes;
Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations;
Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization's implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Описание: Starting with an easy introduction to KNIME Analytics Platform, this book will take you through the key features of the platform and cover the advanced and latest deep learning concepts in neural networks. In each chapter, you`ll solve real-world case studies based on deep learning networks to spark your creativity for new projects.
Автор: Omelianenko Iaroslav Название: Hands-On Neuroevolution with Python ISBN: 183882491X ISBN-13(EAN): 9781838824914 Издательство: Неизвестно Рейтинг: Цена: 9378.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you to apply popular neuroevolution strategies to existing neural network designs to improve their performance. It covers practical examples in areas such as games, robotics, and simulation of natural processes, using real-world examples and data sets for your better understanding.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples
Key Features
Understand how to use PyTorch 1.x to build advanced neural network models
Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques
Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more
Book Description
Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.
By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
What You Will Learn
Implement text and music generating models using PyTorch
Build a deep Q-network (DQN) model in PyTorch
Export universal PyTorch models using Open Neural Network Exchange (ONNX)
Become well-versed with rapid prototyping using PyTorch with fast.ai
Perform neural architecture search effectively using AutoML
Easily interpret machine learning (ML) models written in PyTorch using Captum
Design ResNets, LSTMs, Transformers, and more using PyTorch
Find out how to use PyTorch for distributed training using the torch.distributed API
Who this book is for
This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Описание: The blockchain revolution has drastically impacted global economics and the strategic practices within different industries. Cryptocurrency specifically has forever changed the face of business and the implementation of business online. While innovative, people are still in the early stages of building and developing blockchain technology and its applications, and it is critical that researchers and practitioners obtain a better understanding of this global phenomenon. Architectures and Frameworks for Developing and Applying Blockchain Technology is an essential reference source that presents the technological foundation, recent research findings, developments, and critical issues associated with blockchain technology from both computer science and social science perspectives. Featuring topics such as artificial intelligence, digital economy, and network technology, this book is ideally designed for academics, researchers, industry leaders, IT consultants, engineers, programmers, practitioners, government officials, policymakers, and students.
Описание: By the end of the decade, approximately 50 billion devices will be connected over the internet using multiple services such as online gaming, ultra-high definition videos, and 5G mobile services. The associated data traffic demand in both fixed and mobile networks is increasing dramatically, causing network operators to have to migrate the existing optical networks towards next-generation solutions. The main challenge within this development stems from network operators having difficulties finding cost-effective next-generation optical network solutions that can match future high capacity demand in terms of data, reach, and the number of subscribers to support multiple network services on a common network infrastructure. Design, Implementation, and Analysis of Next Generation Optical Networks: Emerging Research and Opportunities is an essential reference source that discusses the next generation of high capacity passive optical access networks (PON) in terms of design, implementation, and analysis and offers a complete reference of technology solutions for next-generation optical networks. Featuring research on topics such as artificial intelligence, electromagnetic interface, and wireless communication, this book is ideally designed for researchers, engineers, scientists, and students interested in understanding, designing, and analyzing the next generation of optical networks.
Автор: Edward J. Rzempoluck Название: Neural Network Data Analysis Using Simulnet™ ISBN: 0387982558 ISBN-13(EAN): 9780387982557 Издательство: Springer Рейтинг: Цена: 11586.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents an introduction to the analysis of data using neural networks. This book discusses neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalized regression neural networks, learning quantizer networks, and self-organizing feature maps.
Going beyond isolated research ideas and design experiences, Designing Network On-Chip Architectures in the Nanoscale Era covers the foundations and design methods of network on-chip (NoC) technology. The contributors draw on their own lessons learned to provide strong practical guidance on various design issues.
Exploring the design process of the network, the first part of the book focuses on basic aspects of switch architecture and design, topology selection, and routing implementation. In the second part, contributors discuss their experiences in the industry, offering a roadmap to recent products. They describe Tilera's TILE family of multicore processors, novel Intel products and research prototypes, and the TRIPS operand network (OPN). The last part reveals state-of-the-art solutions to hardware-related issues and explains how to efficiently implement the programming model at the network interface. In the appendix, the microarchitectural details of two switch architectures targeting multiprocessor system-on-chips (MPSoCs) and chip multiprocessors (CMPs) can be used as an experimental platform for running tests.
A stepping stone to the evolution of future chip architectures, this volume provides a how-to guide for designers of current NoCs as well as designers involved with 2015 computing platforms. It cohesively brings together fundamental design issues, alternative design paradigms and techniques, and the main design tradeoffs-consistently focusing on topics most pertinent to real-world NoC designers.
Автор: Zhang, Lei Chen, Le Название: Cloud data center network architectures and technologies ISBN: 0367695707 ISBN-13(EAN): 9780367695705 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes in detail the architectural design, technical implementation, planning and design, and deployment suggestions for cloud Data Center Networks (DCNs) based on the service challenges faced by cloud DCNs.
Автор: Zhang, Lei Название: Cloud Data Center Network Architectures and Technologies ISBN: 0367697750 ISBN-13(EAN): 9780367697754 Издательство: Taylor&Francis Рейтинг: Цена: 7348.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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