  Continual Learning (CL) is an emerging machine learning paradigm that aims to
learn from a continuous stream of tasks without forgetting knowledge learned
from the previous tasks. To avoid performance decrease caused by forgetting,
prior studies exploit episodic memory (EM), which stores a subset of the past
observed samples while learning from new non-i.i.d. data. Despite the promising
results, since CL is often assumed to execute on mobile or IoT devices, the EM
size is bounded by the small hardware memory capacity and makes it infeasible
to meet the accuracy requirements for real-world applications. Specifically,
all prior CL methods discard samples overflowed from the EM and can never
retrieve them back for subsequent training steps, incurring loss of information
that would exacerbate catastrophic forgetting. We explore a novel hierarchical
EM management strategy to address the forgetting issue. In particular, in
mobile and IoT devices, real-time data can be stored not just in high-speed
RAMs but in internal storage devices as well, which offer significantly larger
capacity than the RAMs. Based on this insight, we propose to exploit the
abundant storage to preserve past experiences and alleviate the forgetting by
allowing CL to efficiently migrate samples between memory and storage without
being interfered by the slow access speed of the storage. We call it Carousel
Memory (CarM). As CarM is complementary to existing CL methods, we conduct
extensive evaluations of our method with seven popular CL methods and show that
CarM significantly improves the accuracy of the methods across different
settings by large margins in final average accuracy (up to 28.4%) while
retaining the same training efficiency.
