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Springer Embedded Deep Learning - Neural Network Processing

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Description

Explore advanced techniques for always-on neural network processing with this comprehensive guide from Springer. This book examines both algorithmic and hardware implementation methods designed to enable embedded deep learning. By utilizing synergetic design approaches across application, algorithm, computer architecture, and circuit levels, readers learn how to reduce the computational costs of deep learning algorithms. The text provides a wide overview of effective solutions for creating energy-efficient neural networks, specifically for battery-constrained wearable devices. To demonstrate the impact of these techniques, the authors present four silicon prototypes for embedded deep learning. This resource is essential for understanding the optimization of neural networks in hardware-constrained environments.

Key Features

This Springer publication covers algorithmic and hardware implementation techniques to enable embedded deep learning.

Learn synergetic design approaches across application, algorithmic, computer architecture, and circuit levels to reduce computational costs.

The content includes four silicon prototypes that demonstrate the real-world impact of embedded deep learning techniques.

Gain a wide overview of effective solutions for developing energy-efficient neural networks for battery-constrained wearable devices.

The text discusses the optimization of neural networks to improve performance in embedded environments.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
03 November 2018
Listed Since
12 July 2018

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