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Improve Image Classification by Convolutional Network on Cambricon

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Benchmarking, Measuring, and Optimizing (Bench 2019)

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Abstract

Cambricon provides us with a complete intelligent application system, how to use this system for deep learning algorithms development is a challenging issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classification network on Cambricon with Cambricon Caffe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inference show, the optimal running time of a common ResNet101 network that classifies the CIFAR-10 dataset on Cambricon is nearly three times faster than the baseline. We hope that this work will provide a simple baseline for further exploration of the performance of convolutional neural network on Cambricon.

P. He and G. Chen—Equal contribution.

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References

  1. Bergstra, J., et al.: Theano: a CPU and GPU math compiler in python. In: Proceedings of the 9th Python in Science Conference, vol. 1, pp. 3–10 (2010)

    Google Scholar 

  2. Chen, M., Chen, T., Chen, Q.: An efficient implementation of the ALS-WR algorithm on x86 CPUs. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 116–122. Springer, Cham (2020)

    Google Scholar 

  3. Chen, Y., Chen, T., Xu, Z., Sun, N., Temam, O.: DianNao family: energy-efficient hardware accelerators for machine learning. Commun. ACM 59(11), 105–112 (2016)

    Article  Google Scholar 

  4. Chen, Y., et al.: DaDianNao: a machine-learning supercomputer. In: Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 609–622. IEEE Computer Society (2014)

    Google Scholar 

  5. Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., Andrew, N.: Deep learning with cots HPC systems. In: International Conference on Machine Learning, pp. 1337–1345 (2013)

    Google Scholar 

  6. Deng, W., Wang, P., Wang, J., Li, C., Guo, M.: PSL: exploiting parallelism, sparsity and locality to accelerate matrix factorization on x86 platforms. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 101–109. Springer, Cham (2020)

    Google Scholar 

  7. Fan, X., Li, H., Cao, W., Wang, L.: DT-CGRA: dual-track coarse-grained reconfigurable architecture for stream applications. In: 2016 26th International Conference on Field Programmable Logic and Applications (FPL), pp. 1–9. IEEE (2016)

    Google Scholar 

  8. Farabet, C., Martini, B., Corda, B., Akselrod, P., Culurciello, E., LeCun, Y.: NeuFlow: a runtime reconfigurable dataflow processor for vision. In: CVPR Workshops, pp. 109–116 (2011)

    Google Scholar 

  9. Gao, W., et al.: AIBench: towards scalable and comprehensive datacenter AI benchmarking. In: Zheng, C., Zhan, J. (eds.) Bench 2018. LNCS, vol. 11459, pp. 3–9. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32813-9_1

    Chapter  Google Scholar 

  10. Gao, W., et al.: AIbench: an industry standard internet service AI benchmark suite. arXiv preprint arXiv:1908.08998 (2019)

  11. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  12. Gong, T., Niu, H.: An implementation of ResNet on the classification of RGB-D images. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 149–155. Springer, Cham (2020)

    Google Scholar 

  13. Hao, T., et al.: Edge AIBench: towards comprehensive end-to-end edge computing benchmarking. In: Zheng, C., Zhan, J. (eds.) Bench 2018. LNCS, vol. 11459, pp. 23–30. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32813-9_3

    Chapter  Google Scholar 

  14. Hao, T., Zheng, Z.: The implementation and optimization of matrix decomposition based collaborative filtering task on x86 platform. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 110–115. Springer, Cham (2020)

    Google Scholar 

  15. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  19. Hou, P., Yu, J., Miao, Y., Tai, Y., Wu, Y., Zhao, C.: RVTensor: a light-weight neural network inference framework based on the RISC-V architecture. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 85–90. Springer, Cham (2020)

    Google Scholar 

  20. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)

    Google Scholar 

  21. Jiang, Z., et al.: HPC AI500: a benchmark suite for HPC AI systems. In: Zheng, C., Zhan, J. (eds.) Bench 2018. LNCS, vol. 11459, pp. 10–22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32813-9_2

    Chapter  Google Scholar 

  22. Kim, J.-Y., Kim, M., Lee, S., Jinwook, O., Kim, K., Yoo, H.-J.: A 201.4 GOPS 496 mW real-time multi-object recognition processor with bio-inspired neural perception engine. IEEE J. Solid-State Circ. 45(1), 32–45 (2009)

    Article  Google Scholar 

  23. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)

    Google Scholar 

  24. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  25. Li, G., Wang, X., Ma, X., Liu, L., Feng, X.: XDN: towards efficient inference of residual neural networks on Cambricon chips. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 51–56. Springer, Cham (2020)

    Google Scholar 

  26. Li, J., Jiang, Z.: Performance analysis of Cambricon MLU100. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 57–66. Springer, Cham (2020)

    Google Scholar 

  27. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  28. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  29. Liu, S., et al.: Cambricon: an instruction set architecture for neural networks. In: ACM SIGARCH Computer Architecture News, vol. 44, pp. 393–405. IEEE Press (2016)

    Google Scholar 

  30. Luo, C., et al.: AIoT Bench: towards comprehensive benchmarking mobile and embedded device intelligence. In: Zheng, C., Zhan, J. (eds.) Bench 2018. LNCS, vol. 11459, pp. 31–35. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32813-9_4

    Chapter  Google Scholar 

  31. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  32. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  33. Wang, F., et al.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2017)

    Google Scholar 

  34. Wang, Y., Zeng, C., Li, C.: Exploring the performance bound of Cambricon accelerator in end-to-end inference scenario. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 67–74. Springer, Cham (2020)

    Google Scholar 

  35. Xiong, X., Wen, X., Huang, C.: Improving RGB-D face recognition via transfer learning from a pretrained 2D network. In: Gao, W., et al. (eds.) Bench 2019. LNCS, vol. 12093, pp. 141–148. Springer, Cham (2020)

    Google Scholar 

  36. Zhang, S., et al.: Cambricon-X: an accelerator for sparse neural networks. In: The 49th Annual IEEE/ACM International Symposium on Microarchitecture, p. 20. IEEE Press (2016)

    Google Scholar 

  37. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)

    Google Scholar 

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Acknowledgements

The authors would like to thank BenchCouncil for BenchCouncil Testbed. The whole team is supported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19020400), Equipment Pre-Research Fund (Grant No. 61403120405, Grant No. 6141B07090131) and Spaceborne Equipment Pre-Research Project (Grant No. 305030704).

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, HaiDian, Beijing, 110108, China

    Peng He, Ge Chen, Kai Deng, Ping Yao & Li Fu

  2. University of Chinese Academy of Sciences, Shijingshan, Beijing, 14430, China

    Peng He, Ge Chen & Kai Deng

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  1. Peng He
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  2. Ge Chen
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  3. Kai Deng
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  4. Ping Yao
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  5. Li Fu
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Corresponding author

Correspondence to Ping Yao .

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Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wanling Gao

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

  3. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Geoffrey Fox

  4. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Xiaoyi Lu

  5. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA

    Dan Stanzione

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He, P., Chen, G., Deng, K., Yao, P., Fu, L. (2020). Improve Image Classification by Convolutional Network on Cambricon. 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_7

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  • DOI: https://doi.org/10.1007/978-3-030-49556-5_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-49556-5

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