Efficient Processing of Deep Neural Networks
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Von Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel S. Emer, im heise Shop in digitaler Fassung erhältlich
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 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 the 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 a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.
* 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
* Authors' Biographies
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