  Various incremental learning (IL) approaches have been proposed to help deep
learning models learn new tasks/classes continuously without forgetting what
was learned previously (i.e., avoid catastrophic forgetting). With the growing
number of deployed audio sensing applications that need to dynamically
incorporate new tasks and changing input distribution from users, the ability
of IL on-device becomes essential for both efficiency and user privacy.
  However, prior works suffer from high computational costs and storage demands
which hinders the deployment of IL on-device. In this work, to overcome these
limitations, we develop an end-to-end and on-device IL framework, FastICARL,
that incorporates an exemplar-based IL and quantization in the context of
audio-based applications. We first employ k-nearest-neighbor to reduce the
latency of IL. Then, we jointly utilize a quantization technique to decrease
the storage requirements of IL. We implement FastICARL on two types of mobile
devices and demonstrate that FastICARL remarkably decreases the IL time up to
78-92% and the storage requirements by 2-4 times without sacrificing its
performance. FastICARL enables complete on-device IL, ensuring user privacy as
the user data does not need to leave the device.
