MCDNN: An approximation-based execution framework for deep stream processing under resource constraints

MobiSys 2016 - Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

Volumn , Issue , 2016, Pages 123-136

  Han, Seungyeop ^a   Shen, Haichen ^a   Philipose, Matthai ^b   Agarwal, Sharad ^b   Wolman, Alec ^b   Krishnamurthy, Arvind ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUDGET CONTROL; COMPUTER VISION; DISTRIBUTED COMPUTER SYSTEMS; ECONOMIC AND SOCIAL EFFECTS; FLOW MEASUREMENT; MOBILE DEVICES; NATURAL RESOURCES MANAGEMENT; PROBLEM SOLVING; RESOURCE ALLOCATION; SCHEDULING;

CLASSIFICATION ACCURACY; COMPUTATIONAL DEMANDS; DEEP NEURAL NETWORKS; DESIGN AND IMPLEMENTATIONS; MULTIPLE CLASSIFICATION; OPERATING CONDITION; OPTIMIZING COMPILERS; RESOURCE MANAGEMENT PROBLEMS;

INFORMATION MANAGEMENT;

EID: 84979939317     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2906388.2906396     Document Type: Conference Paper

References (52)

1. Pwrcheck manual. http://www. westmountainradio.com/pdf/PWRcheckManual.pdf, 2015.
2. Meerkatstreams website. http://meerkatstreams.com/, 2016.
3. NVIDIA Jetson TK1 development board. http://www. nvidia.com/object/jetson-tk1-embedded-dev-kit.html, 2016.
4. NVIDIA shield website. http://shield.nvidia.com/, 2016.
5. Twitter periscope website. http://www.periscope.tv, 2016.
6. S. Bambach, J. M. Franchak, D. J. Crandall, and C. Yu. Detecting hands in childrens egocentric views to understand embodied attention during social interaction. Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), pages 134-139, 2014.
7. N. Bansal, N. Buchbinder, and J. Naor. A primal-dual randomized algorithm for weighted paging. Journal of the ACM (JACM), 59(4): 19, 2012.
8. N. Buchbinder and J. Naor. Online primal-dual algorithms for covering and packing. Mathematics of Operations Research, 34(2): 270-286, 2009.
9. C. Chekuri and S. Khanna. A polynomial time approximation scheme for the multiple knapsack problem. SIAM Journal on Computing, 35(3): 713-728, 2005.
10. K. Chellapilla, S. Puri, and P. Simard. High performance convolutional neural networks for document processing. In Proceedings of the 10th InternationalWorkshop on Frontiers in Handwriting Recognition (IWFHR), 2006.
11. T. Chen, Z. Du, N. Sun, J.Wang, C.Wu,Y. Chen, and O. Temam. DianNao: A small-footprint high-throughput accelerator for ubiquitous machine-learning. In Proceedings of the 9th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2014.
12. T.Y.-H. Chen, L. Ravindranath, S. Deng, P. Bahl, and H. Balakrishnan. Glimpse: Continuous, real-time object recognition on mobile devices. In Proceedings of The 13th ACM Conference on Embedded Networked Sensor Systems (SenSys), 2015.
13. W. Chen, J. T.Wilson, S. Tyree, K. Q.Weinberger, and Y. Chen. Compressing neural networks with the hashing trick. In Proceedings of the 32nd International Conference on Machine Learning (ICML), pages 2285-2294, 2015.
14. Chen, Yu-Hsin and Krishna, Tushar and Emer, Joel and Sze, Vivienne. Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. In Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC), pages 262-263, 2016.
15. T. M. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman. Project adam: Building an efficient and scalable deep learning training system. In Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2014.
16. E. Cuervo, A. Balasubramanian, D. ki Cho, A.Wolman, S. Saroiu, R. Chandra, and P. Bahl. Maui: Making smartphones last longer with code offload. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys), 2010.
17. J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. V. Le, M. Z. Mao, M. Ranzato, A.W. Senior, P. A. Tucker, K. Yang, and A. Y. Ng. Large scale distributed deep networks. In Proceedings of the Twenty-sixth Annual Conference on Neural Information Processing Systems (NIPS), 2012.
18. J. Deng,W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 248-255, 2009.
19. K. Ha, Z. Chen,W. Hu,W. Richter, P. Pillai, and M. Satyanarayanan. Towards wearable cognitive assistance. In Proceedings of the 12th International Conference on Mobile Systems, Applications, and Services (MobiSys), 2014.
20. S. Han, H. Mao, andW. J. Dally. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In Proceedings of the International Conference on Learning Representations (ICLR), 2016.
21. S. Han, R. Nandakumar, M. Philipose, A. Krishnamurthy, and D.Wetherall. GlimpseData: Towards continuous vision-based personal analytics. In Proceedings of the 1stWorkshop on Physical Analytics (WPA), 2014.
22. S. Han, J. Pool, J. Tran, andW. Dally. Learning both weights and connections for efficient neural network. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Proceedings of the Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), pages 1135-1143. 2015.
23. H. Hoffmann. Jouleguard: Energy guarantees for approximate applications. In Proceedings of the 25th ACM Symposium on Operating Systems Principles (SOSP), pages 198-214, 2015.
24. J. Huang, F. Qian, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. A close examination of performance and power characteristics of 4g LTE networks. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 225-238, 2012.
25. M. Jaderberg, A.Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. In Proceedings of the British Machine Vision Conference (BMVC), 2014.
26. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (MM), 2014.
27. A. Karpathy, A. Joulin, and L. Fei-Fei. Deep fragment embeddings for bidirectional image-sentence mapping. In Proceedings of the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS), 2014.
28. Y. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin. Compression of deep convolutional neural networks for fast and low power mobile applications. In Proceedings of the International Conference on Learning Representations (ICLR), 2016.
29. M. Kristan et al. The visual object tracking vot2015 challenge results. In IEEE International Conference on Computer VisionWorkshops (ICCVW) - Visual Object Tracking Challenge (VOT), 2015.
30. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Proceedings of the Twenty-sixth Annual Conference on Neural Information Processing Systems (NIPS), 2012.
31. N. D. Lane, P. Georgiev, and L. Qendro. DeepEar: Robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pages 283-294, 2015.
32. X. Lei, A. Senior, A. Gruenstein, and J. Sorensen. Accurate and compact large vocabulary speech recognition on mobile devices. In Proceedings of the 14th Annual Conference of the International Speech Communication Association (Interspeech), 2013.
33. H. Li, Z. Lin, X. Shen, J. Brandt, and G. Hua. A convolutional neural network cascade for face detection. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5325-5334, 2015.
34. R. LiKamWa and L. Zhong. Starfish: Efficient concurrency support for computer vision applications. In Proceedings of the 13th International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 213-226, 2015.
35. S. Liu, Z. Du, J. Tao, D. Han, T. Luo, Y. Xie, Y. Chen, and T. Chen. Diannaoyu: An instruction set architecture for neural networks. In Proceedings of the 43rd International Symposium on Computer Architecture (ISCA), 2016.
36. S. Nath. ACE: Exploiting correlation for energy-efficient and continuous context sensing. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 29-42, 2012.
37. H. Pirsiavash and D. Ramanan. Detecting activities of daily living in first-person camera views. In Computer Vision and Pattern Recognition (CVPR), pages 2847-2854, 2012.
38. M.-R. Ra, A. Sheth, L. Mummert, P. Pillai, D.Wetherall, and R. Govindan. Odessa: Enabling interactive perception applications on mobile devices. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys), 2011.
39. S. Rallapalli, A. Ganesan, K. Chintalapudi, V. N. Padmanabhan, and L. Qiu. Enabling physical analytics in retail stores using smart glasses. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (MobiCom), pages 115-126, 2014.
40. M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi. XNORNet: ImageNet Classification Using Binary Convolutional Neural Networks. arXiv: 1603.05279, Mar. 2016.
41. J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection. In Computer Vision and Pattern Recognition (CVPR), 2016.
42. A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio. Fitnets: Hints for thin deep nets. In Proceedings of the International Conference on Learning Representations (ICLR), 2015.
43. A. A. Sani, K. Boos, M. H. Yun, and L. Zhong. Rio: A system solution for sharing I/O between mobile systems. In Proceedings of the 12th International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 259-272, 2014.
44. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations (ICLR), 2015.
45. C. Szegedy,W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
46. Y. Taigman, M. Yang, M. Ranzato, and L.Wolf. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
47. R. Tapu, B. Mocanu, and T. B. Zaharia. ALICE: A smartphone assistant used to increase the mobility of visual impaired people. Journal of Ambient Intelligence and Smart Environments (JAISE), 7(5): 659-678, 2015.
48. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001.
49. J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.
50. D. Yu, J. Li, D. Yu, M. Seltzer, and Y. Gong. Singular value decomposition based low-footprint speaker adaptation and personalization for deep neural network. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
51. D. Yu, F. Seide, G. Li, and L. Deng. Exploiting sparseness in deep neural networks for large vocabulary speech recognition. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.
52. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva. Learning deep features for scene recognition using places database. In Proceedings of the Twenty-eighth Annual Conference on Neural Information Processing Systems (NIPS), 2014.