Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning

Comparison experimental results with existing runtime and static pruning methods on state-of-the-art CNNs demonstrate that the proposed framework is able to provide a tradeoff between dynamic flexibility and storage efficiency in runtime channel pruning.