  Real-time on-device continual learning is needed for new applications such as
home robots, user personalization on smartphones, and augmented/virtual reality
headsets. However, this setting poses unique challenges: embedded devices have
limited memory and compute capacity and conventional machine learning models
suffer from catastrophic forgetting when updated on non-stationary data
streams. While several online continual learning models have been developed,
their effectiveness for embedded applications has not been rigorously studied.
In this paper, we first identify criteria that online continual learners must
meet to effectively perform real-time, on-device learning. We then study the
efficacy of several online continual learning methods when used with mobile
neural networks. We measure their performance, memory usage, compute
requirements, and ability to generalize to out-of-domain inputs.
