  We are witnessing an increasing availability of streaming data that may
contain valuable information on the underlying processes. It is thus attractive
to be able to deploy machine learning models on edge devices near sensors such
that decisions can be made instantaneously, rather than first having to
transmit incoming data to servers. To enable deployment on edge devices with
limited storage and computational capabilities, the full-precision parameters
in standard models can be quantized to use fewer bits. The resulting quantized
models are then calibrated using back-propagation and full training data to
ensure accuracy. This one-time calibration works for deployments in static
environments. However, model deployment in dynamic edge environments call for
continual calibration to adaptively adjust quantized models to fit new incoming
data, which may have different distributions. The first difficulty in enabling
continual calibration on the edge is that the full training data may be too
large and thus not always available on edge devices. The second difficulty is
that the use of back-propagation on the edge for repeated calibration is too
expensive. We propose QCore to enable continual calibration on the edge. First,
it compresses the full training data into a small subset to enable effective
calibration of quantized models with different bit-widths. We also propose
means of updating the subset when new streaming data arrives to reflect changes
in the environment, while not forgetting earlier training data. Second, we
propose a small bit-flipping network that works with the subset to update
quantized model parameters, thus enabling efficient continual calibration
without back-propagation. An experimental study, conducted with real-world data
in a continual learning setting, offers insight into the properties of QCore
and shows that it is capable of outperforming strong baseline methods.
