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

by Bert Moons, Daniel Bankman, Marian Verhelst

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.

222 pages, Paperback

Published January 19, 2019