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Elsevier Hardware Accelerator Systems for AI & Machine Learning - Volume 122

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Description

Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 from Academic Press explores the transformative impact of Deep Neural Networks and Machine Learning on modern computing. This comprehensive volume examines hardware accelerator systems designed to optimise AI and machine learning workloads, covering critical topics including GPU-based deep learning, edge computing optimisation, and specialised tensor processing architectures. The book provides detailed analysis of NPU architecture for DNNs and introduces foundational concepts in hardware acceleration. Whether you're an engineer, researcher, or software architect seeking to understand how specialised hardware enhances AI performance, this volume delivers technical insights into the systems and architectures that power modern artificial intelligence applications. Perfect for those looking to bridge the gap between machine learning algorithms and their efficient hardware implementation.

Key Features

Explores hardware accelerator systems specifically designed for artificial intelligence and machine learning workloads.

Covers GPU-based deep learning techniques and their application in modern AI systems.

Examines edge computing optimisation strategies for deploying deep learning models on specialised tensor processing architectures.

Details the architecture of NPUs for DNN processing and their role in accelerating machine learning inference.

Published by Academic Press as Volume 122 in the Advances in Computers series for authoritative technical content.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
11 April 2021
Listed Since
01 February 2020

Barcode

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