Format:
1 Online-Ressource (1 PDF (xxi, 319 pages))
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illustrations (some color)
Edition:
Also available in print
ISBN:
9781681738321
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9781681738338
Series Statement:
Synthesis lectures in computer architecture #50
Content:
part I. Understanding deep neural networks -- 1. Introduction -- 1.1. Background on deep neural networks -- 1.2. Training versus inference -- 1.3. Development history -- 1.4. Applications of DNNs -- 1.5. Embedded versus cloud
Content:
2. Overview of deep neural networks -- 2.1. Attributes of connections within a layer -- 2.2. Attributes of connections between layers -- 2.3. Popular types of layers in DNNs -- 2.4. Convolutional neural networks (CNNs) -- 2.5. Other DNNs -- 2.6. DNN development resources
Content:
part II. Design of hardware for processing DNNs -- 3. Key metrics and design objectives -- 3.1. Accuracy -- 3.2. Throughput and latency -- 3.3. Energy efficiency and power consumption -- 3.4. Hardware cost -- 3.5. Flexibility -- 3.6. Scalability -- 3.7. Interplay between different metrics
Content:
4. Kernel computation -- 4.1. Matrix multiplication with Toeplitz -- 4.2. Tiling for optimizing performance -- 4.3. Computation transform optimizations -- 4.4. Summary
Content:
5. Designing DNN accelerators -- 5.1. Evaluation metrics and design objectives -- 5.2. Key properties of DNN to leverage -- 5.3. DNN hardware design considerations -- 5.4. Architectural techniques for exploiting data reuse -- 5.5. Techniques to reduce reuse distance -- 5.6. Dataflows and loop nests -- 5.7. Dataflow taxonomy -- 5.8. DNN accelerator buffer management strategies -- 5.9. Flexible NoC design for DNN accelerators -- 5.10. Summary
Content:
6. Operation mapping on specialized hardware -- 6.1. Mapping and loop nests -- 6.2. Mappers and compilers -- 6.3. Mapper organization -- 6.4. Analysis framework for energy efficiency -- 6.5. Eyexam : framework for evaluating performance -- 6.6. Tools for map space exploration
Content:
part III. Co-design of DNN hardware and algorithms -- 7. Reducing precision -- 7.1. Benefits of reduce precision -- 7.2. Determining the bit width -- 7.3. Mixed precision : different precision for different data types -- 7.4. Varying precision : change precision for different parts of the DNN -- 7.5. Binary nets -- 7.6. Interplay between precision and other design choices -- 7.7. Summary of design considerations for reducing precision
Content:
8. Exploiting sparsity -- 8.1. Sources of sparsity -- 8.2. Compression -- 8.3. Sparse dataflow -- 8.4. Summary
Content:
9. Designing efficient DNN models -- 9.1. Manual network design -- 9.2. Neural architecture search -- 9.3. Knowledge distillation -- 9.4. Design considerations for efficient DNN models
Content:
10. Advanced technologies -- 10.1. Processing near memory -- 10.2. Processing in memory -- 10.3. Processing in sensor -- 10.4. Processing in the optical domain -- 11. Conclusion.
Content:
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
Note:
Part of: Synthesis digital library of engineering and computer science
,
Includes bibliographical references (pages 283-316)
,
Compendex
,
INSPEC
,
Google scholar
,
Google book search
,
Also available in print.
,
Mode of access: World Wide Web.
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System requirements: Adobe Acrobat Reader.
Additional Edition:
ISBN 9781681738352
Additional Edition:
ISBN 9781681738314
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9781681738352
Additional Edition:
ISBN 9781681738314
Language:
English
Subjects:
Economics
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