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About Efficient Processing of Deep Neural Networks (Synthesis Lectures on Computer Architecture)
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 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.
Detailed Information
| Author: | Vivienne Sze |
|---|---|
| Publication Year: | 2020 |
| ISBN: | 9781681738314 |
| Pages: | 341 |
| Language: | English |
| File Size: | 21 |
| Format: | |
| Price: | FREE |
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