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  3. Elektronik: kredse og komponenter

Efficient Processing of Deep Neural Networks

Af: Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze, Joel S. Emer Engelsk Paperback

Efficient Processing of Deep Neural Networks

Af: Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze, Joel S. Emer Engelsk Paperback
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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.

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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.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 254
ISBN-13: 9783031006388
Indbinding: Paperback
Udgave:
ISBN-10: 3031006380
Kategori: Elektronik: kredse og komponenter
Udg. Dato: 24 jun 2020
Længde: 0mm
Bredde: 191mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 24 jun 2020
Forfatter(e): Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze, Joel S. Emer
Forfatter(e) Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze, Joel S. Emer

Kategori Elektronik: kredse og komponenter

ISBN-13 9783031006388

Sprog Engelsk

Indbinding Paperback

Sider 254

Udgave

Længde 0mm

Bredde 191mm

Højde 235mm

Udg. Dato 24 jun 2020

Oplagsdato 24 jun 2020

Forlag Springer International Publishing AG

Kategori sammenhænge