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Deep learning for computer architects

Author: Brandon Reagen; Robert Adolf, (Computer scientist); Paul Whatmough; Gu-Yeon Wei; David Brooks
Publisher: [San Rafael, California] : Morgan & Claypool Publishers, [2017] ©2017
Series: Synthesis lectures in computer architecture, #41.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
DEEP LEARNING FOR COMPUTER ARCHITECTS.
[S.l.] : MORGAN & CLAYPOOL, 2017
(OCoLC)1002527664
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Brandon Reagen; Robert Adolf, (Computer scientist); Paul Whatmough; Gu-Yeon Wei; David Brooks
ISBN: 9781627059855 1627059857 1627057285 9781627057288
OCLC Number: 1001572030
Description: 1 online resource (xiv, 109 pages) : illustrations (some color)
Contents: 1. Introduction --
1.1 The rises and falls of neural networks --
1.2 The third wave --
1.2.1 A virtuous cycle --
1.3 The role of hardware in deep learning --
1.3.1 State of the practice --
2. Foundations of deep learning --
2.1 Neural networks --
2.1.1 Biological neural networks --
2.1.2 Artificial neural networks --
2.1.3 Deep neural networks --
2.2 Learning --
2.2.1 Types of learning --
2.2.2 How deep neural networks learn --
3. Methods and models --
3.1 An overview of advanced neural network methods --
3.1.1 Model architectures --
3.1.2 Specialized layers --
3.2 Reference workloads for modern deep learning --
3.2.1 Criteria for a deep learning workload suite --
3.2.2 The fathom workloads --
3.3 Computational intuition behind deep learning --
3.3.1 Measurement and analysis in a deep learning framework --
3.3.2 Operation type profiling --
3.3.3 Performance similarity --
3.3.4 Training and inference --
3.3.5 Parallelism and operation balance --
4. Neural network accelerator optimization: a case study --
4.1 Neural networks and the simplicity wall --
4.1.1 Beyond the wall: bounding unsafe optimizations --
4.2 Minerva: a three-pronged approach --
4.3 Establishing a baseline: safe optimizations --
4.3.1 Training space exploration --
4.3.2 Accelerator design space --
4.4 Low-power neural network accelerators: unsafe optimizations --
4.4.1 Data type quantization --
4.4.2 Selective operation pruning --
4.4.3 SRAM fault mitigation --
4.5 Discussion --
4.6 Looking forward --
5. A literature survey and review --
5.1 Introduction --
5.2 Taxonomy --
5.3 Algorithms --
5.3.1 Data types --
5.3.2 Model sparsity --
5.4 Architecture --
5.4.1 Model sparsity --
5.4.2 Model support --
5.4.3 Data movement --
5.5 Circuits --
5.5.1 Data movement --
5.5.2 Fault tolerance --
6. Conclusion --
Bibliography --
Authors' biographies.
Series Title: Synthesis lectures in computer architecture, #41.
Responsibility: Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks.
More information:

Abstract:

A primer for computer architects in a new and rapidly evolving field. The authors review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to  Read more...

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