Presentation
ODILO: On-Device Incremental Learning Via Lightweight Operations
DescriptionIn this paper, we propose ODILO, a new on-device incremental
learning framework for edge systems. The key part of ODILO is a new module, namely Efficient Incremental Module (EIM). EIM is composed of normal convolutions and lightweight operations. During incremental learning, EIM exploits some lightweight operations, called adapters, to effectively and efficiently learn features for new classes such that it can improve the accuracy of incremental learning while reducing model complexity as well as training overhead. The efficiency of ODILO is further bolstered by adapter fusion, prototypes, and efficient data augmentation. We conduct extensive experiments on the CIFAR-100 and Tiny-ImageNet datasets. Experimental results show that ODILO improves the accuracy by up to 4.21% over existing methods while reducing around 50% of model complexity. In addition, evaluations on real edge systems demonstrate its applicability for on-device machine learning. The code will be available upon acceptance.
learning framework for edge systems. The key part of ODILO is a new module, namely Efficient Incremental Module (EIM). EIM is composed of normal convolutions and lightweight operations. During incremental learning, EIM exploits some lightweight operations, called adapters, to effectively and efficiently learn features for new classes such that it can improve the accuracy of incremental learning while reducing model complexity as well as training overhead. The efficiency of ODILO is further bolstered by adapter fusion, prototypes, and efficient data augmentation. We conduct extensive experiments on the CIFAR-100 and Tiny-ImageNet datasets. Experimental results show that ODILO improves the accuracy by up to 4.21% over existing methods while reducing around 50% of model complexity. In addition, evaluations on real edge systems demonstrate its applicability for on-device machine learning. The code will be available upon acceptance.
Event Type
Work-in-Progress Poster
TimeTuesday, June 256:00pm - 7:00pm PDT
LocationLevel 2 Lobby
AI
Autonomous Systems
Cloud
Design
EDA
Embedded Systems
IP
Security