Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
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Achieving good speed and accuracy trade-off on target platform is very important in deploying deep neural networks. Most existing automatic architecture search approaches only pursue high performance but ignores such an important factor. In this work, we propose an algorithm "Partial Order Pruning" to prune architecture search space with partial order assumption, quickly lift the boundary of speed/accuracy trade-off on target platform, and automatically search the architecture with the best speed and accuracy trade-off. Our algorithm explicitly take profile information about the inference speed on target platform into consideration. With the proposed algorithm, we present several "Dongfeng" networks that provide high accuracy and fast inference speed on various application GPU platforms. By further searching decoder architecture, our DF-Seg real-time segmentation models yields state-of-the-art speed/accuracy trade-off on both embedded device and high-end GPU.
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