Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded Platforms Article Swipe
Wei Lou
,
Xun Lei
,
Amin Sabet
,
Jia Bi
,
Jonathon Hare
,
Geoff V. Merrett
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/cvprw53098.2021.00347
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/cvprw53098.2021.00347
Mobile and embedded platforms are increasingly required to efficiently execute computationally demanding DNNs across heterogeneous processing elements. At runtime, the available hardware resources to DNNs can vary considerably due to other concurrently running applications. The performance requirements of the applications could also change under different scenarios. To achieve the desired performance, dynamic DNNs have been proposed in which the number of channels/layers can be scaled in real time to meet different requirements under varying resource constraints. However, the training process of such dynamic DNNs can be costly, since platform-aware models of different deployment scenarios must be retrained to become dynamic. This paper proposes Dynamic-OFA, a novel dynamic DNN approach for state-of-the-art platform-aware NAS models (i.e. Once-for-all network (OFA)). Dynamic-OFA pre-samples a family of sub-networks from a static OFA backbone model, and contains a runtime manager to choose different sub-networks under different runtime environments. As such, Dynamic-OFA does not need the traditional dynamic DNN training pipeline. Compared to the state-of-the-art, our experimental results using ImageNet on a Jetson Xavier NX show that the approach is up to 3.5x (CPU), 2.4x (GPU) faster for similar Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/cvprw53098.2021.00347
- OA Status
- green
- Cited By
- 34
- References
- 40
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3175549063Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/cvprw53098.2021.00347Digital Object Identifier
- Title
-
Dynamic-OFA: Runtime DNN Architecture Switching for Performance Scaling on Heterogeneous Embedded PlatformsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-01Full publication date if available
- Authors
-
Wei Lou, Xun Lei, Amin Sabet, Jia Bi, Jonathon Hare, Geoff V. MerrettList of authors in order
- Landing page
-
https://doi.org/10.1109/cvprw53098.2021.00347Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://eprints.soton.ac.uk/448645/1/Dynamic_OFA_CVPR_W_2021_Accepted.pdfDirect OA link when available
- Concepts
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Computer science, Pipeline (software), Latency (audio), Software deployment, Process (computing), State (computer science), Distributed computing, Embedded system, Real-time computing, Parallel computing, Operating system, Algorithm, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
34Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 4, 2023: 14, 2022: 9, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6638667902, https://openalex.org/W3035204081, https://openalex.org/W6780827055, https://openalex.org/W6753767121, https://openalex.org/W2965658867, https://openalex.org/W2963163009, https://openalex.org/W2963918968, https://openalex.org/W6781600403, https://openalex.org/W3035251378, https://openalex.org/W2945558825, https://openalex.org/W2108598243, https://openalex.org/W6756887525, https://openalex.org/W6753490951, https://openalex.org/W2194775991, https://openalex.org/W6737664043, https://openalex.org/W2982083293, https://openalex.org/W6767064347, https://openalex.org/W6735260822, https://openalex.org/W3012511001, https://openalex.org/W2962861284, https://openalex.org/W3108411658, https://openalex.org/W2981698279, https://openalex.org/W6763381322, https://openalex.org/W6757036269, https://openalex.org/W2886851211, https://openalex.org/W2949941638, https://openalex.org/W1836465849, https://openalex.org/W2905741102, https://openalex.org/W2883780447, https://openalex.org/W2963766446, https://openalex.org/W3105260221, https://openalex.org/W4297775537, https://openalex.org/W2612445135, https://openalex.org/W3109154950, https://openalex.org/W2964259004, https://openalex.org/W2947681860, https://openalex.org/W2994749257, https://openalex.org/W2598097916, https://openalex.org/W3044604993, https://openalex.org/W2949117887 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 104, 120, 125, 132, 165 |
| abstract_inverted_index.As | 143 |
| abstract_inverted_index.At | 17 |
| abstract_inverted_index.NX | 168 |
| abstract_inverted_index.To | 46 |
| abstract_inverted_index.at | 192 |
| abstract_inverted_index.be | 63, 85, 95 |
| abstract_inverted_index.in | 56, 65 |
| abstract_inverted_index.is | 173 |
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| abstract_inverted_index.on | 164 |
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| abstract_inverted_index.up | 174 |
| abstract_inverted_index.DNN | 107, 152 |
| abstract_inverted_index.NAS | 112 |
| abstract_inverted_index.OFA | 127 |
| abstract_inverted_index.The | 34 |
| abstract_inverted_index.and | 1, 130 |
| abstract_inverted_index.are | 4 |
| abstract_inverted_index.can | 25, 62, 84 |
| abstract_inverted_index.due | 28 |
| abstract_inverted_index.for | 109, 181 |
| abstract_inverted_index.not | 147 |
| abstract_inverted_index.our | 159 |
| abstract_inverted_index.the | 19, 38, 48, 58, 77, 149, 157, 171 |
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| abstract_inverted_index.3.5x | 176 |
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| abstract_inverted_index.5.1% | 188 |
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| abstract_inverted_index.This | 100 |
| abstract_inverted_index.also | 41 |
| abstract_inverted_index.been | 54 |
| abstract_inverted_index.does | 146 |
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| abstract_inverted_index.have | 53 |
| abstract_inverted_index.meet | 69 |
| abstract_inverted_index.must | 94 |
| abstract_inverted_index.need | 148 |
| abstract_inverted_index.real | 66 |
| abstract_inverted_index.show | 169 |
| abstract_inverted_index.such | 81 |
| abstract_inverted_index.that | 170 |
| abstract_inverted_index.time | 67 |
| abstract_inverted_index.vary | 26 |
| abstract_inverted_index.(GPU) | 179, 189 |
| abstract_inverted_index.(i.e. | 114 |
| abstract_inverted_index.Top-1 | 183 |
| abstract_inverted_index.could | 40 |
| abstract_inverted_index.novel | 105 |
| abstract_inverted_index.other | 30 |
| abstract_inverted_index.paper | 101 |
| abstract_inverted_index.since | 87 |
| abstract_inverted_index.such, | 144 |
| abstract_inverted_index.under | 43, 72, 139 |
| abstract_inverted_index.using | 162 |
| abstract_inverted_index.which | 57 |
| abstract_inverted_index.(CPU), | 177, 187 |
| abstract_inverted_index.Jetson | 166 |
| abstract_inverted_index.Mobile | 0 |
| abstract_inverted_index.Xavier | 167 |
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| abstract_inverted_index.family | 121 |
| abstract_inverted_index.faster | 180 |
| abstract_inverted_index.higher | 190 |
| abstract_inverted_index.model, | 129 |
| abstract_inverted_index.models | 89, 113 |
| abstract_inverted_index.number | 59 |
| abstract_inverted_index.scaled | 64 |
| abstract_inverted_index.static | 126 |
| abstract_inverted_index.(OFA)). | 117 |
| abstract_inverted_index.achieve | 47 |
| abstract_inverted_index.costly, | 86 |
| abstract_inverted_index.desired | 49 |
| abstract_inverted_index.dynamic | 51, 82, 106, 151 |
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| abstract_inverted_index.manager | 134 |
| abstract_inverted_index.network | 116 |
| abstract_inverted_index.process | 79 |
| abstract_inverted_index.results | 161 |
| abstract_inverted_index.running | 32 |
| abstract_inverted_index.runtime | 133, 141 |
| abstract_inverted_index.similar | 182, 193 |
| abstract_inverted_index.varying | 73 |
| abstract_inverted_index.Compared | 155 |
| abstract_inverted_index.However, | 76 |
| abstract_inverted_index.ImageNet | 163 |
| abstract_inverted_index.accuracy | 191 |
| abstract_inverted_index.approach | 108, 172 |
| abstract_inverted_index.backbone | 128 |
| abstract_inverted_index.contains | 131 |
| abstract_inverted_index.dynamic. | 99 |
| abstract_inverted_index.embedded | 2 |
| abstract_inverted_index.hardware | 21 |
| abstract_inverted_index.latency. | 194 |
| abstract_inverted_index.proposed | 55 |
| abstract_inverted_index.proposes | 102 |
| abstract_inverted_index.required | 6 |
| abstract_inverted_index.resource | 74 |
| abstract_inverted_index.runtime, | 18 |
| abstract_inverted_index.training | 78, 153 |
| abstract_inverted_index.accuracy, | 184 |
| abstract_inverted_index.available | 20 |
| abstract_inverted_index.demanding | 11 |
| abstract_inverted_index.different | 44, 70, 91, 137, 140 |
| abstract_inverted_index.elements. | 16 |
| abstract_inverted_index.pipeline. | 154 |
| abstract_inverted_index.platforms | 3 |
| abstract_inverted_index.resources | 22 |
| abstract_inverted_index.retrained | 96 |
| abstract_inverted_index.scenarios | 93 |
| abstract_inverted_index.deployment | 92 |
| abstract_inverted_index.processing | 15 |
| abstract_inverted_index.scenarios. | 45 |
| abstract_inverted_index.Dynamic-OFA | 118, 145 |
| abstract_inverted_index.efficiently | 8 |
| abstract_inverted_index.performance | 35 |
| abstract_inverted_index.pre-samples | 119 |
| abstract_inverted_index.traditional | 150 |
| abstract_inverted_index.Dynamic-OFA, | 103 |
| abstract_inverted_index.Once-for-all | 115 |
| abstract_inverted_index.applications | 39 |
| abstract_inverted_index.concurrently | 31 |
| abstract_inverted_index.considerably | 27 |
| abstract_inverted_index.constraints. | 75 |
| abstract_inverted_index.experimental | 160 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.performance, | 50 |
| abstract_inverted_index.requirements | 36, 71 |
| abstract_inverted_index.sub-networks | 123, 138 |
| abstract_inverted_index.applications. | 33 |
| abstract_inverted_index.environments. | 142 |
| abstract_inverted_index.heterogeneous | 14 |
| abstract_inverted_index.platform-aware | 88, 111 |
| abstract_inverted_index.channels/layers | 61 |
| abstract_inverted_index.computationally | 10 |
| abstract_inverted_index.state-of-the-art | 110 |
| abstract_inverted_index.state-of-the-art, | 158 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.93071788 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |