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

• Main Authors: Moons, Bert (Author, http://id.loc.gov/vocabulary/relators/aut), Bankman, Daniel (http://id.loc.gov/vocabulary/relators/aut), Verhelst, Marian (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Subjects: Electronic circuits.; Signal processing.; Image processing.; Speech processing systems.; Electronics.; Microelectronics.; Circuits and Systems.; Signal, Image and Speech Processing.; Electronics and Microelectronics, Instrumentation.

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...

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.