Abstract
Deep neural networks (DNNs) are capable of achieving high performance in various tasks. However, the huge number of parameters and floating point operations make it difficult to deploy them on edge devices. Therefore, in recent years, a lot of researches have been done to reduce the weight of deep convolutional neural networks. Conventional research prunes based on a set of criteria, but we do not know if those criteria are optimal or not. In order to solve this problem, this paper proposes a method to select parameters for pruning automatically. Specifically, all parameter information is input, and reinforcement learning is used to select and prune parameters that do not affect the accuracy. Our method prunes one filter or node in one action and compresses it by repeating the action. The proposed method was able to highly compress the CNN with minimal degradation in accuracy and reduce about 97.0% of the parameters with 2.53% degradation in CIFAR10 image classification task on VGG16.
Published Version
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