£86.64

Elsevier Deep Learning on Edge Computing Devices - Book

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Description

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture provides a technical look at hardware architecture and embedded deep learning. This book is designed to help researchers increase the performance of Edge-deep learning models for mobile computing and various other applications. Readers will explore neural network algorithms alongside hardware design optimization approaches specifically for Edge-deep learning environments. The text provides practical context by introducing specific applications within each section to ground theoretical concepts in real-world use cases. To conclude the study, the book presents a comprehensive example involving smart surveillance cameras. This final section integrates innovations in both algorithm and architecture, offering a complete view of how these technologies work together in a single system. This resource is an essential guide for those working at the intersection of computer science, AI, and machine learning.

Key Features

Focuses on hardware architecture and embedded deep learning including neural networks to support mobile computing research.

Provides neural network algorithms and hardware design optimization approaches for Edge-deep learning performance.

Includes specific applications in each section to demonstrate how theory applies to real-world computing scenarios.

Features a comprehensive example of smart surveillance cameras to integrate algorithm and architecture innovations.

Designed for researchers looking to maximize the efficiency of deep learning models on edge computing devices.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
11 February 2022
Listed Since
08 September 2021

Barcode

No barcode data available

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