Performance Analysis and Characterization of Training Deep Learning Models on Mobile Device
Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enhancing user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models,...
Uloženo v:
| Vydáno v: | 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) s. 506 - 515 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.12.2019
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enhancing user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models, but not training. The performance characterization of training deep learning models on mobile devices is largely unexplored, although understanding the performance characterization is critical for designing and implementing deep learning models on mobile devices. In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models. We introduce a benchmark suite and a tool to study performance of training deep learning models on mobile devices, from the perspectives of memory consumption, hardware utilization, and power consumption. The tool can correlate performance results with fine-grained operations in deep learning models, providing capabilities to capture performance variance and problems at a fine granularity. We reveal interesting performance problems and opportunities, including under-utilization of heterogeneous hardware, large energy consumption of the memory, and high predictability of workload characterization. Based on the performance analysis, we suggest interesting research directions. |
|---|---|
| AbstractList | Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enhancing user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models, but not training. The performance characterization of training deep learning models on mobile devices is largely unexplored, although understanding the performance characterization is critical for designing and implementing deep learning models on mobile devices. In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models. We introduce a benchmark suite and a tool to study performance of training deep learning models on mobile devices, from the perspectives of memory consumption, hardware utilization, and power consumption. The tool can correlate performance results with fine-grained operations in deep learning models, providing capabilities to capture performance variance and problems at a fine granularity. We reveal interesting performance problems and opportunities, including under-utilization of heterogeneous hardware, large energy consumption of the memory, and high predictability of workload characterization. Based on the performance analysis, we suggest interesting research directions. |
| Author | Liu, Jie Liu, Jiawen Du, Wan Li, Dong |
| Author_xml | – sequence: 1 givenname: Jie surname: Liu fullname: Liu, Jie organization: University of California, Merced – sequence: 2 givenname: Jiawen surname: Liu fullname: Liu, Jiawen organization: University of California, Merced – sequence: 3 givenname: Wan surname: Du fullname: Du, Wan organization: University of California, Merced – sequence: 4 givenname: Dong surname: Li fullname: Li, Dong organization: University of California, Merced |
| BookMark | eNotjMFKw0AURUfQhdZ-gSDzA6lvZpK-mWVI1RZSLFhXLspL8qID6aRMglC_3qCuDpdzOTfiMvSBhbhXsFAK3MOm2OWr1xQtLhcalFsAAOKFmDu0CrVVOrNGXYv3Hce2j0cKNcs8UHce_CApNLL4pEj1yNF_0-j7IPtW7iP54MOHXDGfZMkUf9e2b7gb5PTZ9pXveNJfvuZbcdVSN_D8nzPx9vS4L9ZJ-fK8KfIy8RrMmLCDLNVkuQGTmdaRqVOuU3BICIYR7NJwRaadBKBztdGq4YxbXSE3ZMxM3P11PTMfTtEfKZ4P1mGGLjM_bWZRwQ |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICPADS47876.2019.00077 |
| 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/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781728125831 1728125839 |
| EndPage | 515 |
| ExternalDocumentID | 8975795 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i203t-e90542a8ed0353f9a3c4ec4097a703e70863eba3fa3c0799c321de5ef2b7eda33 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 40 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000530854900068&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Thu Jun 29 18:38:56 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-e90542a8ed0353f9a3c4ec4097a703e70863eba3fa3c0799c321de5ef2b7eda33 |
| PageCount | 10 |
| ParticipantIDs | ieee_primary_8975795 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-Dec |
| PublicationDateYYYYMMDD | 2019-12-01 |
| PublicationDate_xml | – month: 12 year: 2019 text: 2019-Dec |
| PublicationDecade | 2010 |
| PublicationTitle | 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) |
| PublicationTitleAbbrev | PADSW |
| PublicationYear | 2019 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 2.300947 |
| Snippet | Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 506 |
| SubjectTerms | deep learning hardware heterogeneity mobile device performance analysis performance characterization |
| Title | Performance Analysis and Characterization of Training Deep Learning Models on Mobile Device |
| URI | https://ieeexplore.ieee.org/document/8975795 |
| WOSCitedRecordID | wos000530854900068&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/eLvHCXMwlV1LSwMxEA5t8eBJpRXf5ODRtXlsNpujtBYFLQtWKXgo2WQiBdktffj7TXbXFsGLt5AJBCZkkknm-z6Ergm1DnjiIiG59AmKCzIvLImYFRJUorWoiOffnuR4nE6nKmuhmy0WBgCq4jO4Dc3qL9-WZhOeyvqpkkIq0UZtKWWN1WpAv5So_uMguxu-BLKZUHpAAw8lkb9VU6pDY3Twv-kOUW-HvsPZ9lw5Qi0ouug921X44x8mEawLiwdbyuUaUYlLhyeN8AMeAixww6H6gYPw2ecK-zHPZe7DgTeHQNFDr6P7yeAhaoQRojkjfB2B8hctplOwhAvulOYmBhOYq7TfwCB9msIh19x5A5FKGc6oBQGO5RKs5vwYdYqygBOErSDUJLGy1EBsHUv9fcrRPJdGU6CxPkXd4JjZoua-mDU-Ofu7-xztB8_X5R4XqLNebuAS7Zmv9Xy1vKoW7Bujd5np |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NS8MwGA5zCnpS2cRvc_BoXZM0TXOUTdlwGwWnDDyMNHkjA-nGPvz9Jm3dELx4C0kgkJD3I3mf50HoNiTGAottwAUTLkGxXuaFxgE1XICMleIF8fxbXwyHyXgs0xq622BhAKAoPoN73yz-8s1Mr_1TWSuRggvJd9AujyJKSrRWBfsloWz12ulD58XTzfjiA-KZKEPxWzelcBtPh_9b8Ag1t_g7nG48yzGqQd5A7-m2xh__cIlglRvc3pAul5hKPLN4VEk_4A7AHFcsqh_YS599LrGbM5hlziC4YW8qmuj16XHU7gaVNEIwpSFbBSBdqEVVAiZknFmpmI5Ae-4q5a4wCJeoMMgUs24gFFJqRokBDpZmAoxi7ATV81kOpwgbHhIdR9IQDZGxNHERlSVZJrQiQCJ1hhp-Yybzkv1iUu3J-d_dN2i_Oxr0J_3e8PkCHfhTKIs_LlF9tVjDFdrTX6vpcnFdHN430aadMA |
| 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=2019+IEEE+25th+International+Conference+on+Parallel+and+Distributed+Systems+%28ICPADS%29&rft.atitle=Performance+Analysis+and+Characterization+of+Training+Deep+Learning+Models+on+Mobile+Device&rft.au=Liu%2C+Jie&rft.au=Liu%2C+Jiawen&rft.au=Du%2C+Wan&rft.au=Li%2C+Dong&rft.date=2019-12-01&rft.pub=IEEE&rft.spage=506&rft.epage=515&rft_id=info:doi/10.1109%2FICPADS47876.2019.00077&rft.externalDocID=8975795 |