Vis-TOP: Visual Transformer Overlay Processor Article Swipe
Wei Hu
,
Dian Xu
,
Zimeng Fan
,
Fang Liu
,
Yanxiang He
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.10957
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2110.10957
In recent years, Transformer has achieved good results in Natural Language Processing (NLP) and has also started to expand into Computer Vision (CV). Excellent models such as the Vision Transformer and Swin Transformer have emerged. At the same time, the platform for Transformer models was extended to embedded devices to meet some resource-sensitive application scenarios. However, due to the large number of parameters, the complex computational flow and the many different structural variants of Transformer models, there are a number of issues that need to be addressed in its hardware design. This is both an opportunity and a challenge. We propose Vis-TOP (Visual Transformer Overlay Processor), an overlay processor for various visual Transformer models. It differs from coarse-grained overlay processors such as CPU, GPU, NPE, and from fine-grained customized designs for a specific model. Vis-TOP summarizes the characteristics of all visual Transformer models and implements a three-layer and two-level transformation structure that allows the model to be switched or changed freely without changing the hardware architecture. At the same time, the corresponding instruction bundle and hardware architecture are designed in three-layer and two-level transformation structure. After quantization of Swin Transformer tiny model using 8-bit fixed points (fix_8), we implemented an overlay processor on the ZCU102. Compared to GPU, the TOP throughput is 1.5x higher. Compared to the existing Transformer accelerators, our throughput per DSP is between 2.2x and 11.7x higher than others. In a word, the approach in this paper meets the requirements of real-time AI in terms of both resource consumption and inference speed. Vis-TOP provides a cost-effective and power-effective solution based on reconfigurable devices for computer vision at the edge.
Related Topics
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2110.10957
- https://arxiv.org/pdf/2110.10957
- OA Status
- green
- Cited By
- 4
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3207100110
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3207100110Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2110.10957Digital Object Identifier
- Title
-
Vis-TOP: Visual Transformer Overlay ProcessorWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-21Full publication date if available
- Authors
-
Wei Hu, Dian Xu, Zimeng Fan, Fang Liu, Yanxiang HeList of authors in order
- Landing page
-
https://arxiv.org/abs/2110.10957Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2110.10957Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2110.10957Direct OA link when available
- Concepts
-
Overlay, Computer science, Transformer, Architecture, Computer hardware, Computer architecture, Engineering, Electrical engineering, Operating system, Voltage, Visual arts, ArtTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ZCU102. | 204 |
| abstract_inverted_index.between | 225 |
| abstract_inverted_index.changed | 159 |
| abstract_inverted_index.complex | 64 |
| abstract_inverted_index.design. | 90 |
| abstract_inverted_index.designs | 129 |
| abstract_inverted_index.devices | 48, 265 |
| abstract_inverted_index.differs | 115 |
| abstract_inverted_index.higher. | 213 |
| abstract_inverted_index.models, | 75 |
| abstract_inverted_index.models. | 113 |
| abstract_inverted_index.others. | 231 |
| abstract_inverted_index.overlay | 107, 118, 200 |
| abstract_inverted_index.propose | 100 |
| abstract_inverted_index.results | 7 |
| abstract_inverted_index.started | 16 |
| abstract_inverted_index.various | 110 |
| abstract_inverted_index.without | 161 |
| abstract_inverted_index.(fix_8), | 196 |
| abstract_inverted_index.Compared | 205, 214 |
| abstract_inverted_index.Computer | 20 |
| abstract_inverted_index.However, | 55 |
| abstract_inverted_index.Language | 10 |
| abstract_inverted_index.achieved | 5 |
| abstract_inverted_index.approach | 236 |
| abstract_inverted_index.changing | 162 |
| abstract_inverted_index.computer | 267 |
| abstract_inverted_index.designed | 178 |
| abstract_inverted_index.embedded | 47 |
| abstract_inverted_index.emerged. | 34 |
| abstract_inverted_index.existing | 217 |
| abstract_inverted_index.extended | 45 |
| abstract_inverted_index.hardware | 89, 164, 175 |
| abstract_inverted_index.platform | 40 |
| abstract_inverted_index.provides | 256 |
| abstract_inverted_index.resource | 250 |
| abstract_inverted_index.solution | 261 |
| abstract_inverted_index.specific | 132 |
| abstract_inverted_index.switched | 157 |
| abstract_inverted_index.variants | 72 |
| abstract_inverted_index.Excellent | 23 |
| abstract_inverted_index.addressed | 86 |
| abstract_inverted_index.different | 70 |
| abstract_inverted_index.inference | 253 |
| abstract_inverted_index.processor | 108, 201 |
| abstract_inverted_index.real-time | 244 |
| abstract_inverted_index.structure | 150 |
| abstract_inverted_index.two-level | 148, 182 |
| abstract_inverted_index.Processing | 11 |
| abstract_inverted_index.challenge. | 98 |
| abstract_inverted_index.customized | 128 |
| abstract_inverted_index.implements | 144 |
| abstract_inverted_index.processors | 119 |
| abstract_inverted_index.scenarios. | 54 |
| abstract_inverted_index.structural | 71 |
| abstract_inverted_index.structure. | 184 |
| abstract_inverted_index.summarizes | 135 |
| abstract_inverted_index.throughput | 210, 221 |
| abstract_inverted_index.Processor), | 105 |
| abstract_inverted_index.Transformer | 3, 29, 32, 42, 74, 103, 112, 141, 189, 218 |
| abstract_inverted_index.application | 53 |
| abstract_inverted_index.consumption | 251 |
| abstract_inverted_index.implemented | 198 |
| abstract_inverted_index.instruction | 172 |
| abstract_inverted_index.opportunity | 95 |
| abstract_inverted_index.parameters, | 62 |
| abstract_inverted_index.three-layer | 146, 180 |
| abstract_inverted_index.architecture | 176 |
| abstract_inverted_index.fine-grained | 127 |
| abstract_inverted_index.quantization | 186 |
| abstract_inverted_index.requirements | 242 |
| abstract_inverted_index.accelerators, | 219 |
| abstract_inverted_index.architecture. | 165 |
| abstract_inverted_index.computational | 65 |
| abstract_inverted_index.corresponding | 171 |
| abstract_inverted_index.coarse-grained | 117 |
| abstract_inverted_index.cost-effective | 258 |
| abstract_inverted_index.reconfigurable | 264 |
| abstract_inverted_index.transformation | 149, 183 |
| abstract_inverted_index.characteristics | 137 |
| abstract_inverted_index.power-effective | 260 |
| abstract_inverted_index.resource-sensitive | 52 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile |