Gpgpu accelerated deep object classification on a heterogeneous mobile platform

Electronics (Switzerland)

Volumn 5, Issue 4, 2016, Pages 1-17

  Rizvi, Syed Tahir Hussain ^a   Cabodi, Gianpiero ^a   Patti, Denis ^a   Francini, Gianluca ^b  

^a POLITECNICO DI TORINO   (Italy)

^b TELECOM ITALIA   (Italy)

Author keywords

Concurrent computing; Convolution; Image analysis; Image processing; Machine vision; Mobile computing; Multicore processing; Neural networks; Ubiquitous computing

Indexed keywords

EID: 85027446278     PISSN: None     EISSN: 20799292     Source Type: Journal    
DOI: 10.3390/ELECTRONICS5040088     Document Type: Article

References (32)

1. Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, 2015. Available online: http://arxiv.org/abs/1409.1556 (accessed on 10 May 2016).
2. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. In Proceedings of The Twenty-sixth Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA 3–8 December 2012; pp. 1097–1105.
3. Asano, S.; Maruyama, T.; Yamaguchi, Y. Performance comparison of FPGA, GPU and CPU in image processing. In Proceedings of the 2009 International Conference on Field Programmable Logic and Applications, Prague, Czech, 31 August–2 September 2009; pp. 126–131.
4. Naik, V.H.; Kusur, C.S. Analysis of performance enhancement on graphic processor based heterogeneous architecture: A CUDA and MATLAB experiment. In Proceedings of the 2015 National Conference on Parallel Computing Technologies (PARCOMPTECH), Bangalore, India, 19–20 February 2015; pp. 1–5.
5. Vandal, N.A.; Savvides, M. CUDA accelerated illumination preprocessing on GPUs. In Proceedings of the 2011 17th International Conference on Digital Signal Processing (DSP), Corfu, Greek, 6–8 July 2011; pp. 1–6.
6. Raghav, S.; Ruggiero, M.; Marongiu, A.; Pinto, C.; Atienza, D.; Benini, L. GPU Acceleration for Simulating Massively Parallel Many-Core Platforms. IEEE Trans. Parallel Distrib. Syst. 2015, 26, 1336–1349.
7. Baek, A.R.; Lee, K.; Choi, H. Speed-up image processing on mobile CPU and GPU. In Proceedings of the 2015 Asia Pacific Conference onMultimedia and Broadcasting (APMediaCast), Kuta, Indonesia, 23–25 April 2015; pp. 1–3.
8. López, M.B.; Nykänen, H.; Hannuksela, J.; Silvén, O.; Vehviläinen, M. Accelerating image recognition on mobile devices using GPGPU. Parallel Process. Imaging Appl. SPIE 2011, 7872, 78720R.
9. Huang, Y.; Wu, R.; Sun, Y.; Wang, W.; Ding, X. Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy. IEEE Trans. Intell. Transp. Syst. 2015, 16, 1951–1960.
10. Abdulnabi, A.H.; Wang, G.; Lu, J.; Jia, K. Multi-Task CNN Model for Attribute Prediction. IEEE Trans. Multimed. 2015, 17, 1949–1959.
11. Rizvi, S.T.H.; Cabodi, G.; Patti, D.; Gulzar, M.M. Comparison of GPGPU based robotic manipulator with other embedded controllers. In Proceedings of the 2016 International Conference on Development and Application Systems (DAS), Suceava, Romania, 19–21 May 2016; pp. 10–15.
12. Satria, M.T. Real-time system-level implementation of a telepresence robot using an embedded GPU platform. In Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 14–18 March 2016; pp. 1445–1448.
13. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. CoRR. 2015. Available online: http://arxiv.org/abs/1512.03385 (accessed on 21 July 2016).
14. Sermanet, P.; Eigen, D.; Zhang, X.; Mathieu, M.; Fergus, R.; LeCun, Y. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. Computer Science—Computer Vision and Pattern Recognition. 2013. Available online: https://arxiv.org/abs/1312.6229 (accessed on 1 August 2016).
15. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.E.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions. CoRR. 2014. Available online: http://arxiv.org/abs/1409.4842 (accessed on 21 July 2016).
16. He, K.; Zhang, X.; Ren, S.; Sun, J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015; pp. 1026–1034.
17. Zeiler, M.D.; Fergus, R. Visualizing and Understanding Convolutional Networks. In Proceedings of the 13th European Conference on Computer Vision—ECCV 2014, Zurich, Switzerland, 6–12 September 2014; Part I, pp. 818–833.
18. Huynh, L.N.; Balan, R.K.; Lee, Y. DeepSense: A GPU-based Deep Convolutional Neural Network Framework on Commodity Mobile Devices. In Proceedings of the 2016 Workshop on Wearable Systems and Applications, Singapore, 30 June 2016; pp. 25–30.
19. Gu, J.; Liu, Y.; Gao, Y.; Zhu, M. OpenCL Caffe: Accelerating and Enabling a Cross Platform Machine Learning Framework. In Proceedings of the 4th International Workshop on OpenCL, Vienna, Austria, 19–21 April 2016; pp. 8:1–8:5.
20. Wu, J.; Leng, C.; Wang, Y.; Hu, Q.; Cheng, J. Quantized Convolutional Neural Networks for Mobile Devices. Computer Science—Computer Vision and Pattern Recognition. 2016. Available online: https://arxiv.org/abs/1512.06473 (accessed on 3 October 2016).
21. Lavin, A. Fast Algorithms for Convolutional Neural Networks. CoRR. 2015. Available online: http://arxiv. org/abs/1509.09308 (accessed on 5 May 2016).
22. Kim, Y.-D.; Park, E.; Yoo, S.; Choi, T.; Yang, L.; Shin, D. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications. CoRR. 2015. Available online: http://arxiv.org/abs/1511. 06530 (accessed on 24 April 2016).
23. Liu, X.; Turakhia, Y. Pruning of Winograd and FFT Convolution Algorithm. Available online: http://cs231n. stanford.edu/reports2016/117_Report.pdf (accessed on 17 May 2016).
24. Zheng, Z.; Li, Z. A Nagar and Kyungmo Park. In Proceedings of the 2015 IEEE International Conference on Compact Deep Neural Networks for Device Based Image Classification, Multimedia & Expo Workshops (ICMEW), Turin, Italy, 29 June–3 July 2015; pp. 1–6.
25. Tsung, P.K.; Tsai, S.F.; Pai, A.; Lai, S.J.; Lu, C. High performance deep neural network on low cost mobile GPU. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 7–11 January 2016; pp. 69–70.
26. Russo, L.M.; Pedrino, E.C.; Kato, E.; Roda, V.O. Image convolution processing: A GPU versus FPGA comparison. In Proceedings of the 2012 VIII Southern Conference on Programmable Logic (SPL), Bento Goncalves, Brazil, 20–23 March 2012; pp. 1–6.
27. Zeiler, M.D.; Fergus, R. Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. CoRR. 2013. Available online: http://arxiv.org/abs/1301.3557 (accessed on 20 July 2016).
28. Cong, J.; Xiao, B. Minimizing Computation in Convolutional Neural Networks. In Proceedings of the 24th International Conference onArtificial Neural Networks and Machine Learning—ICANN 2014, Hamburg, Germany, 15–19 September 2014; pp. 281–290.
29. Chellapilla, K.; Puri, S.; Simard, P. High Performance Convolutional Neural Networks for Document Processing. Guy Lorette. In Proceedings of the Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule, France, 23–26 October 2006.
30. Jiang, W.; Chen, Y.; Jin, H.; Luo, B.; Chi, Y. A Novel Fast Approach for Convolutional Networks with Small Filters Based on GPU. In Proceedings of the 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), New York, NY, USA, 24–26 August 2015; pp. 278–283.
31. Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. CoRR. 2015. Available online: http://arxiv.org/abs/1502.03167 (accessed on 21 April 2016).
32. Hara, K.; Saito, D.; Shouno, H. Analysis of function of rectified linear unit used in deep learning. In Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 12–17 July 2015; pp. 1–8.