Efficient continual learning at the edge with progressive segmented training

Xiaocong Du, Shreyas Kolala Venkataramanaiah, Zheng Li, Han Sok Suh, Shihui Yin, Gokul Krishnan, Frank Liu, Jae Sun Seo, Yu Cao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


There is an increasing need for continual learning in dynamic systems at the edge, such as self-driving vehicles, surveillance drones, and robotic systems. Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference, within a limited power budget. Different from previous continual learning algorithms with dynamic structures, this work focuses on a single network and model segmentation to mitigate catastrophic forgetting problem. Leveraging the redundant capacity of a single network, model parameters for each task are separated into two groups: one important group which is frozen to preserve current knowledge, and a secondary group to be saved (not pruned) for future learning. A fixed-size memory containing a small amount of previously seen data is further adopted to assist the training. Without additional regularization, the simple yet effective approach of progressive segmented training (PST) successfully incorporates multiple tasks and achieves state-of-the-art accuracy in the single-head evaluation on the CIFAR-10 and CIFAR-100 datasets. Moreover, the segmented training significantly improves computation efficiency in continual learning and thus, enabling efficient continual learning at the edge. On Intel Stratix-10 MX FPGA, we further demonstrate the efficiency of PST with representative CNNs trained on CIFAR-10.

Original languageEnglish (US)
Article number044006
JournalNeuromorphic Computing and Engineering
Issue number4
StatePublished - Dec 1 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.


  • acquisitive learning
  • brain inspiration
  • continual learning
  • deep neural network
  • model adaption


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