Efficient Processing of Deep Neural Networks
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Efficient Processing of Deep Neural Networks, Morgan & Claypool Publishers Von Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer, im heise shop in digitaler Fassung erhältlich
Produktinformationen "Efficient Processing of Deep Neural Networks"
This book provides a structured treatment of the key principles and techniques
for enabling efficient processing of deep neural networks (DNNs). DNNs are
currently widely used for many artificial intelligence (AI) applications,
including computer vision, speech recognition, and robotics. While DNNs deliver
state-of-the-art accuracy on many AI tasks, it comes at the cost of high
computational complexity. Therefore, techniques that enable efficient processing
of deep neural networks to improve key metrics—such as energy-efficiency,
throughput, and latency—without sacrificing accuracy or increasing hardware
costs are critical to enabling the wide deployment of DNNs in AI systems.
The book includes background on DNN processing; a description and taxonomy of
hardware architectural approaches for designing DNN accelerators; key metrics
for evaluating and comparing different designs; features of DNN processing that
are amenable to hardware/algorithm co-design to improve energy efficiency and
throughput; and opportunities for applying new technologies. Readers will find a
structured introduction to the field as well as formalization and organization
of key concepts from contemporary work that provide insights that may spark new
ideas.
* Preface
* Acknowledgments
* Introduction
* Overview of Deep Neural Networks
* Key Metrics and Design Objectives
* Kernel Computation
* Designing DNN Accelerators
* Operation Mapping on Specialized Hardware
* Reducing Precision
* Exploiting Sparsity
* Designing Efficient DNN Models
* Advanced Technologies
* Conclusion
* Bibliography
* Authors' Biographies
Artikel-Details
- Anbieter:
- Morgan & Claypool Publishers
- Autor:
- Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer
- Artikelnummer:
- 9781681738352
- Veröffentlicht:
- 24.06.20
- Seitenanzahl:
- 341