RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics

Mehrdad Khani, Ganesh Ananthanarayanan, Kevin Hsieh, Junchen Jiang, Ravi Netravali, Yuanchao Shu, Mohammad Alizadeh, Victor Bahl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Continuous learning has recently shown promising results for video analytics by adapting a lightweight “expert” DNN model for each specific video scene to cope with the data drift in real time. However, current adaptation approaches either rely on periodic retraining and suffer its delay and significant compute costs or rely on selecting historical models and incur accuracy loss by not fully leveraging the potential of persistent retraining. Without dynamically optimizing the resource sharing among model selection and retraining, both approaches have a diminishing return at scale. RECL is a new video-analytics framework that carefully integrates model reusing and online model retraining, allowing it to quickly adapt the expert model given any video frame samples. To do this, RECL (i) shares across edge devices a (potentially growing) “model zoo” that comprises expert models previously trained for all edge devices, enabling history model reuse across video sessions, (ii) uses a fast procedure to online select a highly accurate expert model from this shared model zoo, and (iii) dynamically optimizes GPU allocation among model retraining, model selection, and timely updates of the model zoo. Our evaluation of RECL over 70 hours of real-world videos across two vision tasks (object detection and classification) shows substantial performance gains compared to prior work, further amplifying over the system lifetime.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PublisherUSENIX Association
Pages917-932
Number of pages16
ISBN (Electronic)9781939133335
StatePublished - 2023
Event20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023 - Boston, United States
Duration: Apr 17 2023Apr 19 2023

Publication series

NameProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023

Conference

Conference20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Country/TerritoryUnited States
CityBoston
Period4/17/234/19/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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