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Poster
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning
Jianda Chen · Shangyu Chen · Sinno Jialin Pan

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #134

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.

Author Information

Jianda Chen (Nanyang Technological University, Singapore)
Shangyu Chen (Nanyang Technological University, Singapore)
Sinno Jialin Pan (Nanyang Technological University, Singapore)

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