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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
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
Gao, W., et al.: AIbench: an industry standard internet service AI benchmark suite. arXiv preprint arXiv:1908.08998 (2019)
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)
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)
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
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)
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)
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)
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)
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
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)
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)
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
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)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)
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)
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)
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)
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)
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)
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)
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
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)
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
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)
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)
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)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-49556-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49555-8
Online ISBN: 978-3-030-49556-5
eBook Packages: Computer ScienceComputer Science (R0)