  In the traditional deep compression framework, iteratively performing network
pruning and quantization can reduce the model size and computation cost to meet
the deployment requirements. However, such a step-wise application of pruning
and quantization may lead to suboptimal solutions and unnecessary time
consumption. In this paper, we tackle this issue by integrating network pruning
and quantization as a unified joint compression problem and then use AutoML to
automatically solve it. We find the pruning process can be regarded as the
channel-wise quantization with 0 bit. Thus, the separate two-step pruning and
quantization can be simplified as the one-step quantization with mixed
precision. This unification not only simplifies the compression pipeline but
also avoids the compression divergence. To implement this idea, we propose the
automated model compression by jointly applied pruning and quantization (AJPQ).
AJPQ is designed with a hierarchical architecture: the layer controller
controls the layer sparsity, and the channel controller decides the bit-width
for each kernel. Following the same importance criterion, the layer controller
and the channel controller collaboratively decide the compression strategy.
With the help of reinforcement learning, our one-step compression is
automatically achieved. Compared with the state-of-the-art automated
compression methods, our method obtains a better accuracy while reducing the
storage considerably. For fixed precision quantization, AJPQ can reduce more
than five times model size and two times computation with a slight performance
increase for Skynet in remote sensing object detection. When mixed-precision is
allowed, AJPQ can reduce five times model size with only 1.06% top-5 accuracy
decline for MobileNet in the classification task.
