Abstract
In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine.
Li, G., Wang, X., Ma, X.—These authors contributed equally to this work.
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Acknowledgment
This work is supported by the National Key R&D Program of China under Grant No. 2017YFB1003103, the Key Program of National Natural Science Foundation of China under Grant No. 61432016, and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant No. 61521092.
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Li, G., Wang, X., Ma, X., Liu, L., Feng, X. (2020). XDN: Towards Efficient Inference of Residual Neural Networks on Cambricon Chips. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_4
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