RepNet: Efficient On-Device Learning via Feature Reprogramming

Li Yang, Adnan Siraj Rakin, Deliang Fan

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

Abstract

Transfer learning, where the goal is to transfer the well-trained deep learning models from a primary source task to a new task, is a crucial learning scheme for on-device machine learning, due to the fact that IoT/edge devices collect and then process massive data in our daily life. However, due to the tiny memory constraint in IoT/edge devices, such on-device learning requires ultra-small training memory footprint, bringing new challenges for memory-efficient learning. Many existing works solve this problem by reducing the number of trainable parameters. However, this doesn't directly translate to memory saving since the major bottleneck is the activations, not parameters. To develop memory-efficient on-device transfer learning, in this work, we are the first to approach the concept of transfer learning from a new perspective of intermediate feature re-programming of a pre-trained model (i.e., backbone). To perform this lightweight and memory-efficient reprogramming, we propose to train a tiny Reprogramming Network (Rep-Net) directly from the new task input data, while freezing the backbone model. The proposed Rep-Net model interchanges the features with the backbone model using an activation connector at regular intervals to mutually benefit both the backbone model and Rep-Net model features. Through extensive experiments, we validate each design specs of the proposed Rep-Net model in achieving highly memory-efficient on-device reprogramming. Our experiments establish the superior performance (i.e., low training memory and high accuracy) of Rep-Net compared to SOTA on-device transfer learning schemes across multiple benchmarks. Code is available at https://github.com/ASU-ESIC-FAN-Lab/RepNet.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherIEEE Computer Society
Pages12267-12276
Number of pages10
ISBN (Electronic)9781665469463
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
Duration: Jun 19 2022Jun 24 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2022-June
ISSN (Print)1063-6919

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period6/19/226/24/22

Keywords

  • Efficient learning and inferences
  • Transfer/low-shot/long-tail learning

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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