Deep learning for computer architects:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[San Rafael, Cal.]
Morgan & Claypool
[2017]
|
| Series: | Synthesis lectures on computer architecture
# 41 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references (pages 91-106) |
| Physical Description: | xiv, 109 Seiten Illustrationen, Diagramme |
| ISBN: | 9781627057288 |
Staff View
MARC
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Record in the Search Index
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|---|---|
| adam_text | Preface....................................................................xiii
1 Introduction.................................................................1
1.1 The Rises and Falls of Neural Networks................................1
1.2 The Third Wave........................................................3
1.2.1 A Virtuous Cycle................................................3
1.3 The Role of Hardware in Deep Learning.................................5
1.3.1 State of the Practice ..........................................5
2 Foundations of Deep Learning.................................................9
2.1 Neural Networks ......................................................9
2.1.1 Biological Neural Networks.....................................10
2.1.2 Artificial Neural Networks.....................................11
2.1.3 Deep Neural Networks...........................................14
2.2 Learning.............................................................15
2.2.1 Types of Learning..............................................17
2.2.2 How Deep Neural Networks Learn.................................18
3 Methods and Models.........................................................25
3.1 An Overview of Advanced Neural Network Methods.......................25
3.1.1 Model Architectures............................................25
3.1.2 Specialized Layers.............................................28
3.2 Reference Workloads for Modern Deep Learning.........................29
3.2.1 Criteria for a Deep Learning Workload Suite...................29
3.2.2 The Fathom Workloads...........................................31
3.3 Computational Intuition behind Deep Learning.........................34
3.3.1 Measurement and Analysis in a Deep Learning Framework.........35
3.3.2 Operation Type Profiling.......................................36
3.3.3 Performance Similarity.........................................38
3.3.4 Training and Inference.........................................39
3.3.5 Parallelism and Operation Balance..............................40
xii
4 Neural Network Accelerator Optimization: A Case Study........................43
4.1 Neural Networks and the Simplicity Wall...............................44
4.1.1 Beyond the Wall: Bounding Unsafe Optimizations..................44
4.2 Minerva: A Three-pronged Approach.....................................46
4.3 Establishing a Baseline: Safe Optimizations...........................49
4.3.1 Training Space Exploration .....................................49
4.3.2 Accelerator Design Space........................................50
4.4 Low-power Neural Network Accelerators: Unsafe Optimizations...........53
4.4.1 Data Type Quantization..........................................53
4.4.2 Selective Operation Pruning.....................................55
4.4.3 SRAM Fault Mitigation...........................................56
4.5 Discussion............................................................60
4.6 Looking Forward.......................................................61
5 A Literature Survey and Review...............................................63
5.1 Introduction..........................................................63
5.2 Taxonomy..............................................................63
5.3 Algorithms............................................................65
5.3.1 Data Types......................................................66
5.3.2 Model Sparsity..................................................67
5.4 Architecture..........................................................70
5.4.1 Model Sparsity..................................................72
5.4.2 Model Support...................................................74
5.4.3 Data Movement...................................................81
5.5 Circuits..............................................................83
5.5.1 Data Movement...................................................83
5.5.2 Fault Tolerance.................................................85
6 Conclusion...................................................................89
Bibliography.................................................................91
Authors’ Biographies........................................................107
|
| any_adam_object | 1 |
| author | Reagen, Brandon |
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| discipline | Informatik |
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| id | DE-604.BV044560049 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:55:54Z |
| institution | BVB |
| isbn | 9781627057288 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029958688 |
| oclc_num | 1011102261 |
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| owner | DE-29T DE-384 DE-739 |
| owner_facet | DE-29T DE-384 DE-739 |
| physical | xiv, 109 Seiten Illustrationen, Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Morgan & Claypool |
| record_format | marc |
| series | Synthesis lectures on computer architecture |
| series2 | Synthesis lectures on computer architecture |
| spelling | Reagen, Brandon Verfasser aut Deep learning for computer architects Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks [San Rafael, Cal.] Morgan & Claypool [2017] xiv, 109 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on computer architecture # 41 Includes bibliographical references (pages 91-106) deep learning neural network accelerators hardware software co-design DNN benchmarking and characterization hardware support for machine learning Machine learning Neural networks (Computer science) Computer architecture Computer architecture fast Machine learning fast Neural networks (Computer science) fast COMPUTERS / General bisacsh Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Adolf, Robert Sonstige oth Whatmough, Paul Sonstige oth Wei, Gu-Yeon Sonstige (DE-588)1142194612 oth Brooks, David 1975- Sonstige (DE-588)130013560 oth Erscheint auch als Online-Ausgabe 978-1-62705-985-5 Synthesis lectures on computer architecture # 41 (DE-604)BV023068349 41 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029958688&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Reagen, Brandon Deep learning for computer architects Synthesis lectures on computer architecture deep learning neural network accelerators hardware software co-design DNN benchmarking and characterization hardware support for machine learning Machine learning Neural networks (Computer science) Computer architecture Computer architecture fast Machine learning fast Neural networks (Computer science) fast COMPUTERS / General bisacsh Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Deep learning for computer architects |
| title_auth | Deep learning for computer architects |
| title_exact_search | Deep learning for computer architects |
| title_full | Deep learning for computer architects Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks |
| title_fullStr | Deep learning for computer architects Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks |
| title_full_unstemmed | Deep learning for computer architects Brandon Reagen, Robert Adolf, Paul Whatmough, Gu-Yeon Wei, David Brooks |
| title_short | Deep learning for computer architects |
| title_sort | deep learning for computer architects |
| topic | deep learning neural network accelerators hardware software co-design DNN benchmarking and characterization hardware support for machine learning Machine learning Neural networks (Computer science) Computer architecture Computer architecture fast Machine learning fast Neural networks (Computer science) fast COMPUTERS / General bisacsh Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | deep learning neural network accelerators hardware software co-design DNN benchmarking and characterization hardware support for machine learning Machine learning Neural networks (Computer science) Computer architecture COMPUTERS / General Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029958688&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023068349 |
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