  Besides accuracy, the model size of convolutional neural networks (CNN)
models is another important factor considering limited hardware resources in
practical applications. For example, employing deep neural networks on mobile
systems requires the design of accurate yet fast CNN for low latency in
classification and object detection. To fulfill the need, we aim at obtaining
CNN models with both high testing accuracy and small size to address resource
constraints in many embedded devices. In particular, this paper focuses on
proposing a generic reinforcement learning-based model compression approach in
a two-stage compression pipeline: pruning and quantization. The first stage of
compression, i.e., pruning, is achieved via exploiting deep reinforcement
learning (DRL) to co-learn the accuracy and the FLOPs updated after layer-wise
channel pruning and element-wise variational pruning via information dropout.
The second stage, i.e., quantization, is achieved via a similar DRL approach
but focuses on obtaining the optimal bits representation for individual layers.
We further conduct experimental results on CIFAR-10 and ImageNet datasets. For
the CIFAR-10 dataset, the proposed method can reduce the size of VGGNet by 9x
from 20.04MB to 2.2MB with a slight accuracy increase. For the ImageNet
dataset, the proposed method can reduce the size of VGG-16 by 33x from 138MB to
4.14MB with no accuracy loss.
