DMC: Differentiable Model Compression for Hardware-Efficient Convolutional Neural Network
- Title
- DMC: Differentiable Model Compression for Hardware-Efficient Convolutional Neural Network
- Authors
- LEE, SUNG GU; HA, MINHO
- Date Issued
- 2020-07-23
- Publisher
- ACM SIGDA
- Abstract
- Hardware-efficient CNN model design can be divided into two stages: training of a large baseline network to achieve high accuracy and applying model compression to create a smaller network, at the possible expense of a slight reduction in accuracy. This paper proposes a new differential model compression (DMC) method based on bilevel optimization to find the importance of channels in a pretrained CNN. Experimental results show that, for model compression for an image classification task, DMC requires only 12 GPU minutes to achieve a similar compression ratio, but with increased image classification accuracy, when cmpared to the previous best method.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/106097
- Article Type
- Conference
- Citation
- Design Automation Conference, 2020-07-23
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