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

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...

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Bibliographic Details
Main Authors: Moons, Bert (Author), Bankman, Daniel (Author), Verhelst, Marian (Author)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Subjects:
Electronic circuits.
Signal processing.
Electronics.
Electronic Circuits and Systems.
Signal, Speech and Image Processing.
Electronics and Microelectronics, Instrumentation.
Online Access:Full-text access
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Table of Contents:
  • Chapter 1 Embedded Deep Neural Networks
  • Chapter 2 Optimized Hierarchical Cascaded Processing
  • Chapter 3 Hardware-Algorithm Co-optimizations
  • Chapter 4 Circuit Techniques for Approximate Computing
  • Chapter 5 ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing
  • Chapter 6 BINAREYE: Digital and Mixed-signal Always-on Binary Neural Network Processing
  • Chapter 7 Conclusions, contributions and future work.

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