In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs). Our DRL-based framework aims to learn a pruning strategy to determine how many and which channels to be pruned in each convolutional layer, depending on each individual input instance at runtime. Unlike existing runtime pruning methods which require to store all channels parameters for inference, our framework can reduce parameters storage consumption by introducing a static pruning component. Comparison experimental results with existing runtime and static pruning methods on state-of-the-art CNNs demonstrate that our proposed framework is able to provide a tradeoff between dynamic flexibility and storage efficiency in runtime channel pruning.

Weaknesses: --- There are one technical error. The Ref [22] is not a dynamic pruning method as claimed in this paper. Ref [22] (Pattern recognition journal, not a arXiv preprint now) had a section devoted to explain how they achieved static pruning. It is, however, approriate to say that the approach in Ref [22] has inspired or been adopted by some dynamic pruning approach. For example, in tables 1 and 2 and subsequent figures, how are "sparsity" measured?

This is an interesting paper that combines static and dynamic pruning of CNN channels, adding an RL agent into the loop is still able to provide an overall speed up in inference. The reviewers were concerned the paper did not describe how the hyperparameters were chosen and that the choice of action space was not optimal, thus the authors are encouraged to further clarify this in subsequent versions of the paper.

In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs). Our DRL-based framework aims to learn a pruning strategy to determine how many and which channels to be pruned in each convolutional layer, depending on each individual input instance at runtime. Unlike existing runtime pruning methods which require to store all channels parameters for inference, our framework can reduce parameters storage consumption by introducing a static pruning component. Comparison experimental results with existing runtime and static pruning methods on state-of-the-art CNNs demonstrate that our proposed framework is able to provide a tradeoff between dynamic flexibility and storage efficiency in runtime channel pruning.