  The continuous changes in the world have resulted in the performance
regression of neural networks. Therefore, continual learning (CL) area
gradually attracts the attention of more researchers. For edge intelligence,
the CL model not only needs to overcome catastrophic for-getting, but also
needs to face the huge challenge of severely limited resources: the lack of
labeled resources and powerful devices. However, the existing classic CL
methods usually rely on a large number of labeled samples to maintain the
plasticity and stability, and the semi-supervised learning methods often need
to pay a large computational and memory overhead for higher accuracy. In
response to these prob-lems, a low-cost semi-supervised CL method named Edge
Hierarchical Memory Learner (EdgeHML) will be proposed. EdgeHML can
effec-tively utilize a large number of unlabeled samples and a small number of
labeled samples. It is based on a hierarchical memory pool, lever-age
multi-level storage structure to store and replay samples. EdgeHML implements
the interaction between different levels through a combination of online and
offline strategies. In addition, in order to further reduce the computational
overhead for unlabeled samples, EdgeHML leverages a progressive learning
method. It reduces the computation cycles of unlabeled samples by controlling
the learning process. The experimental results show that on three
semi-supervised CL tasks, EdgeHML can improve the model accuracy by up to
16.35% compared with the classic CL method, and the training iterations time
can be reduced by more than 50% compared with semi-supervised methods. EdgeHML
achieves a semi-supervised CL process with high performance and low overhead
for edge intelligence.
