DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices
As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make str...
Uložené v:
| Vydané v: | Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 1097 - 1107 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.05.2022
|
| Predmet: | |
| ISSN: | 1530-2075 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods. |
|---|---|
| AbstractList | As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods. |
| Author | Guan, Yongjie Hou, Xueyu Han, Tao Zhang, Ning |
| Author_xml | – sequence: 1 givenname: Xueyu surname: Hou fullname: Hou, Xueyu email: xh29@njit.edu organization: New Jersey Institute of Technology,USA – sequence: 2 givenname: Yongjie surname: Guan fullname: Guan, Yongjie email: yg274@njit.edu organization: New Jersey Institute of Technology,USA – sequence: 3 givenname: Tao surname: Han fullname: Han, Tao email: tao.han@njit.edu organization: New Jersey Institute of Technology,USA – sequence: 4 givenname: Ning surname: Zhang fullname: Zhang, Ning email: ning.zhang@uwindsor.ca organization: Windsor University,Canada |
| BookMark | eNotj81KAzEURqMo2NY-gQh5gRnvTSaZiTtpqxaKFqoLVyXJ3CnRminzU_HtrdXVgW9xON-QncU6EmPXCCkimJv5crpcKakFpgKESAEO8wkbm7xArVVWIGhzygaoJCQCcnXBhm37DiBAZmbA3qah7ZpZuaFbvtoRlSFueL_jkzru623fhTraLX-ivjmi-6qbDz6PFTUUPfE68qMguL6jkv96-JT2wVN7yc4ru21p_M8Re72fvUwek8Xzw3xyt0jCIaFLrEUPuS-ldE6XnlzmK2EJcxJUWKN0BgC6wsKjNWiFUwYKZ3LpZCUypeSIXf15AxGtd034tM332hSHs2jkD9oJVVQ |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/IPDPS53621.2022.00110 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9781665481069 1665481064 |
| EISSN | 1530-2075 |
| EndPage | 1107 |
| ExternalDocumentID | 9820719 |
| Genre | orig-research |
| GroupedDBID | 29O 6IE 6IF 6IH 6IK 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i203t-aa1c07cd33bb6dceb4cf2ae17e2e8a95640006f18c1a91a2b5908b973b3f24553 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 41 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000854096200102&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 02:25:33 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-aa1c07cd33bb6dceb4cf2ae17e2e8a95640006f18c1a91a2b5908b973b3f24553 |
| PageCount | 11 |
| ParticipantIDs | ieee_primary_9820719 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-May |
| PublicationDateYYYYMMDD | 2022-05-01 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-May |
| PublicationDecade | 2020 |
| PublicationTitle | Proceedings - IEEE International Parallel and Distributed Processing Symposium |
| PublicationTitleAbbrev | IPDPS |
| PublicationYear | 2022 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0020349 |
| Score | 2.0868726 |
| Snippet | As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1097 |
| SubjectTerms | Adaptive systems Computational modeling convolutional neural net-work deep reinforcement learning distributed computing Distributed processing Distribution strategy edge computing Laboratories Portable computers Reinforcement learning |
| Title | DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices |
| URI | https://ieeexplore.ieee.org/document/9820719 |
| WOSCitedRecordID | wos000854096200102&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwEA5z-ODT1E38TR58tC5J0ybx1W24l1GYwnwaSXqVgXRjrvv7TdJaEXzxKSWUC9yld1zv7vsQuktFylNGZAQ8NxFXBY20cFmKEgSMoc7uuiabELOZXCxU1kH37SwMAITmM3jwj6GWn69t5X-VDZULV8JjfB4IkdazWm1y5XFWmgkdStRwmo2yeeK8s88BGQsFB_KLQSUEkEnvf0cfo8HPJB7O2hhzgjpQnqLeNxUDbr7MPnobeQDccf4Oj3i-qd_G1QY7MfvmcukP7JE4whJav_G0PWJd4iDAk19Bjr0cPILgRAbodTJ-eXqOGtaEaOWUsIu0ppYIm8exMWluwXBbMA1UAAOpXTrEfYgqqLRUK6qZ8aznRonYxAXjSRKfoW65LuEc4VRxZ2CmEqIpLyTXMrHEUF9K5CmV8gL1vaaWmxoYY9ko6fLv7St05E1Rdwteo-5uW8ENOrT73epzexus-QV7sKB8 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwEA5jCvo0dRN_mwcfrWvStEl8dRsbzlHYhPk0kvQqA9nG3Pb3m6S1IvjiU0ooF7hL77je3fchdJfwhCU0FAGwTAdM5iRQ3GYpkoegNbF2VwXZBB-NxHQq0xq6r2ZhAMA3n8GDe_S1_Gxptu5XWVvacMUdxueeY84qp7Wq9MohrZQzOiSU7UHaScex9c8uC6TUlxzCXxwqPoT0Gv87_Ai1fmbxcFpFmWNUg8UJanyTMeDy22yit46DwO1m7_CIx6vibbxdYStmV14v9YEdFodffPM3HlRHLBfYC3D0V5BhJwd3wLuRFnrtdSdP_aDkTQjmVgmbQCliQm6yKNI6yQxoZnKqgHCgIJRNiJgLUjkRhihJFNWO91xLHukopyyOo1NUXywXcIZwIpk1MZVxqAjLBVMiNqEmrpjIEiLEOWo6Tc1WBTTGrFTSxd_bt-igP3kZzoaD0fMlOnRmKXoHr1B9s97CNdo3u838c33jLfsFBrqjxQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+-+IEEE+International+Parallel+and+Distributed+Processing+Symposium&rft.atitle=DistrEdge%3A+Speeding+up+Convolutional+Neural+Network+Inference+on+Distributed+Edge+Devices&rft.au=Hou%2C+Xueyu&rft.au=Guan%2C+Yongjie&rft.au=Han%2C+Tao&rft.au=Zhang%2C+Ning&rft.date=2022-05-01&rft.pub=IEEE&rft.eissn=1530-2075&rft.spage=1097&rft.epage=1107&rft_id=info:doi/10.1109%2FIPDPS53621.2022.00110&rft.externalDocID=9820719 |