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...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2019, 2019
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| Edition: | 1st ed. 2019 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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