  Continual learning (CL) trains NN models incrementally from a continuous
stream of tasks. To remember previously learned knowledge, prior studies store
old samples over a memory hierarchy and replay them when new tasks arrive. Edge
devices that adopt CL to preserve data privacy are typically energy-sensitive
and thus require high model accuracy while not compromising energy efficiency,
i.e., cost-effectiveness. Our work is the first to explore the design space of
hierarchical memory replay-based CL to gain insights into achieving
cost-effectiveness on edge devices. We present Miro, a novel system runtime
that carefully integrates our insights into the CL framework by enabling it to
dynamically configure the CL system based on resource states for the best
cost-effectiveness. To reach this goal, Miro also performs online profiling on
parameters with clear accuracy-energy trade-offs and adapts to optimal values
with low overhead. Extensive evaluations show that Miro significantly
outperforms baseline systems we build for comparison, consistently achieving
higher cost-effectiveness.
