Embedded Deep Learning: Algorithms, Architectures and Circuits for Always-On Neural Network Processing

by Marian Verhelst, Bert Moons, Daniel Bankman

Book Overview

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.

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• Format: Paperback
• Language: English
• ISBN: 303007577X
• ISBN13: 9783030075774
• Release Date: January 2019
• Publisher: Springer
• Length: 206 Pages
• Weight: 0.70 lbs.
• Dimensions: 0.5" x 6.1" x 9.2"