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Elsevier Deep Learning on Edge Computing Devices - Algorithm & Architecture

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Description

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture is an Elsevier resource that addresses the intersection of hardware architecture and embedded deep learning for edge computing applications. This comprehensive guide explores neural network algorithms and hardware design optimisation strategies specifically tailored for maximising performance in resource-constrained environments like mobile devices and IoT systems. The book presents practical applications throughout, with smart surveillance cameras featured as an integrated case study that demonstrates real-world implementation of edge-based deep learning. Researchers and engineers working on mobile computing, embedded systems, and AI deployment will find detailed insights into balancing algorithmic efficiency with hardware constraints to achieve optimal performance in edge computing scenarios.

Key Features

Elsevier publication covering hardware architecture and embedded deep learning design for edge computing devices.

Neural network algorithms and hardware optimisation approaches specifically designed for edge-deep learning performance.

Comprehensive smart surveillance camera case study demonstrating integrated algorithm and hardware innovation.

Practical applications and design challenges presented throughout to guide real-world implementation.

Product Specifications

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

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No barcode data available

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