Fluid Dynamic DNNs for Reliable and Adaptive Distributed Inference on Edge Devices Article Swipe
Xun Lei
,
Mingyu Hu
,
Hengrui Zhao
,
Amit Kumar Singh
,
Jonathon Hare
,
Geoff V. Merrett
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.08943
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.08943
Distributed inference is a popular approach for efficient DNN inference at the edge. However, traditional Static and Dynamic DNNs are not distribution-friendly, causing system reliability and adaptability issues. In this paper, we introduce Fluid Dynamic DNNs (Fluid DyDNNs), tailored for distributed inference. Distinct from Static and Dynamic DNNs, Fluid DyDNNs utilize a novel nested incremental training algorithm to enable independent and combined operation of its sub-networks, enhancing system reliability and adaptability. Evaluation on embedded Arm CPUs with a DNN model and the MNIST dataset, shows that in scenarios of single device failure, Fluid DyDNNs ensure continued inference, whereas Static and Dynamic DNNs fail. When devices are fully operational, Fluid DyDNNs can operate in either a High-Accuracy mode and achieve comparable accuracy with Static DNNs, or in a High-Throughput mode and achieve 2.5x and 2x throughput compared with Static and Dynamic DNNs, respectively.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.08943
- https://arxiv.org/pdf/2401.08943
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391013212
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Raw OpenAlex JSON
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https://openalex.org/W4391013212Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.08943Digital Object Identifier
- Title
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Fluid Dynamic DNNs for Reliable and Adaptive Distributed Inference on Edge DevicesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-17Full publication date if available
- Authors
-
Xun Lei, Mingyu Hu, Hengrui Zhao, Amit Kumar Singh, Jonathon Hare, Geoff V. MerrettList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.08943Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.08943Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2401.08943Direct OA link when available
- Concepts
-
Inference, Computer science, Adaptability, MNIST database, Reliability (semiconductor), Throughput, Enhanced Data Rates for GSM Evolution, Distributed computing, Artificial intelligence, Artificial neural network, Wireless, Telecommunications, Biology, Physics, Power (physics), Ecology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.introduce | 32 |
| abstract_inverted_index.operation | 62 |
| abstract_inverted_index.scenarios | 87 |
| abstract_inverted_index.Evaluation | 71 |
| abstract_inverted_index.comparable | 119 |
| abstract_inverted_index.inference, | 96 |
| abstract_inverted_index.inference. | 41 |
| abstract_inverted_index.throughput | 134 |
| abstract_inverted_index.Distributed | 0 |
| abstract_inverted_index.distributed | 40 |
| abstract_inverted_index.incremental | 54 |
| abstract_inverted_index.independent | 59 |
| abstract_inverted_index.reliability | 24, 68 |
| abstract_inverted_index.traditional | 14 |
| abstract_inverted_index.adaptability | 26 |
| abstract_inverted_index.operational, | 107 |
| abstract_inverted_index.High-Accuracy | 115 |
| abstract_inverted_index.adaptability. | 70 |
| abstract_inverted_index.respectively. | 141 |
| abstract_inverted_index.sub-networks, | 65 |
| abstract_inverted_index.High-Throughput | 127 |
| abstract_inverted_index.distribution-friendly, | 21 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |