  As edge devices become prevalent, deploying Deep Neural Networks (DNN) on
edge devices has become a critical issue. However, DNN requires a high
computational resource which is rarely available for edge devices. To handle
this, we propose a novel model compression method for the devices with limited
computational resources, called PQK consisting of pruning, quantization, and
knowledge distillation (KD) processes. Unlike traditional pruning and KD, PQK
makes use of unimportant weights pruned in the pruning process to make a
teacher network for training a better student network without pre-training the
teacher model. PQK has two phases. Phase 1 exploits iterative pruning and
quantization-aware training to make a lightweight and power-efficient model. In
phase 2, we make a teacher network by adding unimportant weights unused in
phase 1 to a pruned network. By using this teacher network, we train the pruned
network as a student network. In doing so, we do not need a pre-trained teacher
network for the KD framework because the teacher and the student networks
coexist within the same network. We apply our method to the recognition model
and verify the effectiveness of PQK on keyword spotting (KWS) and image
recognition.
