Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Computer Vision
Resumo
Deep Learning methods are currently the state-of-the-art in many Computer Vision prob- lems. This 6-hour tutorial explores Deep Learning for Computer Vision through a hands- on approach. Participants will have the opportunity to apply deep neural networks (DNNs) to image classification problems through tools, frameworks and data pipelines commonly used to train and deploy DNN in a customised GPU-accelerated virtual machine. A sur- vey paper will be prepared to bring further details on the topics covered.
Palavras-chave:
deep learning, domain specific architectures, DSAs, computer vision, machine learning, representational learning, deep neural network, computer architecture, edge computing
Referências
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J. Fowers et al., "A Configurable Cloud-Scale DNN Processor for Real-Time AI", Proc. of ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 1-14, 2018.
Amir Yazdanbakhsh et al., GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks.
Nandita Vijaykumar et al., "The Locality Descriptor: A Holistic Cross-Layer Abstraction to Express Data Locality in GPUs", ISCA, 2018.
G. Lan, S. Lee, Y. Zhou, Communication-efficient algorithms for decentralized and stochastic optimization, 2017.
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Pouyanfar Samira, Sadiq Saad, Yan Yilin, Tian Haiman, Tao Yudong, Reyes Maria Presa, Shyu Mei-Ling, Chen Shu-Ching, S. S. Iyengar, "A Survey on Deep Learning: Algorithms Techniques and Applications", ACM Comput. Surv., vol. 51, no. 5, pp. 36, September 2018.
Cho Kyunghyun, Merrienboer Bart van, Gulehre Aglar, Dzmitry Bah-danau, Bougares Fethi, Schwenk Holger, Yoshua Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", The Conference on Empirical Methods in Natural Language Processing, pp. 17241734, 2014.
Li Xiangang, Wu Xihong, "Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition", IEEE International Conference on Acoustics Speech and Signal Processing, pp. 45204524, 2015.
K. He et al., "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770778, June 2016.
Goodfellow Ian, Jean Pouget-Abadie, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron, Bengio Yoshua, "Generative adversarial nets", Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014), pp. 26722680.
Yann Le Cun, Yoshua Bengio, "Convolutional networks for images speech and time series" in The handbook of brain theory and neural networks, vol. 3361.10, no. 1995, 1995.
LeCun Yann, Bengio Yoshua, Geoffrey Hinton, "Deep learning", nature, vol. 521, no. 7553, pp. 436, 2015.
S. Narang, G. Diamos, Baidu DeepBench, 2017, [online] Available: https://github.com/baidu-research/DeepBench.
K. He et al., "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770778, June 2016.
J. Clark, Google Turning Its Lucrative Web Search Over to AI Machines. Bloomberg Technology, October 2015.
Y. Wu et al., Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, September 2016, [online] Available: http://arxiv.org/abs/1609.08144.
D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, "Mastering the game of Go with deep neural networks and tree search", Nature, vol. 529, no. 7587, 2016.
Pouyanfar Samira, Sadiq Saad, Yan Yilin, Tian Haiman, Tao Yudong, Reyes Maria Presa, Shyu Mei-Ling, Chen Shu-Ching, S. S. Iyengar, "A Survey on Deep Learning: Algorithms Techniques and Applications", ACM Comput. Surv., vol. 51, no. 5, pp. 36, September 2018.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, J. Collomosse, "Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask", 2017 30th SIBGRAPI Conference on Graphics Patterns and Images Tutorials (SIBGRAPI-T), pp. 17-41, 2017.
Karen Simonyan, Andrew Zisserman, "Networks for Large-scale Networks for Large-scale image recognition", Proc. of ICLR, 2015.
KrizhevskyIlya, A. Sutskever, E. Geoffrey, ImageNet Classification with Deep Convolutional Neural Networks December 2011Advances in neural information processing systems, vol. 25, no. 2.
John Hennessy, Patterson David, "A new golden age for computer architecture: Domain-specific hardware/software co-design en-hanced security open instruction sets and agile chip development", ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 27-29, 2018, 2018.
M. Bedford Taylor, "The Evolution of Bitcoin Hardware", Computer, vol. 50, no. 9, pp. 58-66, 2017.
C. Tan, M. Karunaratne, T. Mitra, L. Peh, "Stitch: Fusible Heterogeneous Accelerators Enmeshed with Many-Core Architecture for Wearables", 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 575-587, 2018.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
David B. Kirk, W. Hwu Wen-mei, "Programming Massively Parallel Processors" in A Hands-On Approach (3rd ed.), San Francisco, CA, USA:Morgan Kaufmann Publishers Inc., 2016.
Li Fei-Fei, R. Fergus, P. Perona, "One-shot learning of object categories", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, April 2006.
R. Socher, M. Ganjoo, C. D. Manning, A. Y. Ng, "Zero-shot learning through cross-modal transfer", 27th Annual Conference on Neural Information Processing Systems (NIPS, 2013.
David G. Lowe et al., "Object recognition from local scale-invariant features", ICCV, pp. 1150-1157, 1999.
Navneet Dalal, Bill Triggs, 2005. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886893.
Hanna M. Wallach, "Topic modeling: beyond bag-of-words", Proceedings of the 23rd international conference on Machine learning. ACM, 2006.
Finale Doshi-Velez, Kim Been, Towards A Rigorous Science of Interpretable Machine Learning, November 2017, [online] Available: https://arxiv.org/abs/1702.08608.
Krizhevsky Alex, Sutskever Ilya, Geoffrey E. Hinton, "ImageNet classification with deep convolutional neural networks", Communications of the ACM, vol. 60, no. 6, pp. 8490.
Goodfellow Ian, Benjio Yoshua, Courville Aaron, "Deep Learning", MIT Press, 2016.
P. Srivastava et al., "PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine-Learning Algorithms", 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 43-56, 2018.
P. Jouppi Norman, Cliff Young, Nishant Patil, A. Patterson David et al., "In-datacenter performance analysis of a Tensor Processing Unit", Proc. 44th Annual International Symposium on Computer Architecture, pp. 1-12, 2017.
J. Fowers et al., "A Configurable Cloud-Scale DNN Processor for Real-Time AI", Proc. of ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 1-14, 2018.
Amir Yazdanbakhsh et al., GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks.
Nandita Vijaykumar et al., "The Locality Descriptor: A Holistic Cross-Layer Abstraction to Express Data Locality in GPUs", ISCA, 2018.
G. Lan, S. Lee, Y. Zhou, Communication-efficient algorithms for decentralized and stochastic optimization, 2017.
A. Agarwal, J. C. Duchi, Distributed delayed stochastic optimization. NIPS, 2011.
Xiangru Lian, Ce Zhang, Huan Zhang, Hsieh Cho-Jui, Wei Zhang, Ji Liu, "Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent", Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS' 17), 2017.
Zhang Quanshi, Wu Ying Nian, Zhu Song-Chun, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8827-8836, 2018.
Zeiler Matthew D., Rob Fergus, "Visualizing and understanding convolutional networks", European conference on computer vision, pp. 818-833, 2014.
E. Protas, J. D. Bratti, J. F. O. Gaya, P. Drews, S. S. C. Botelho, "Visualization Methods for Image Transformation Convolutional Neural Networks", IEEE Transactions on Neural Networks and Learning Systems.
Liu Chenxi, Zoph Barret, Neumann Maxim, Shlens Jonathon, Hua Wei, Li Li-Jia, Li Fei-Fei, Yuille Alan, Huang Jonathan, Kevin Murphy, The European Conference on Computer Vision (ECCV), pp. 19-34, 2018.
Elsken Thomas, Metzen Jan Hendrik, Frank Hutter, "Neural Architecture Search: A Survey", Journal of Machine Learning Research, vol. 20, pp. 1-21, 2019.
Maryam, M. Najafabadi, Villanustre Flavio, Taghi M. Khoshgoftaar, Seliya Naeem, Wald Randall, Edin Muharemagic, "Deep learning applications and challenges in big data analytics", Journal of Big Data, vol. 2, no. 1, pp. 121, 2015.
Sami Abu-El-Haija, Kothari Nisarg, Lee Joonseok, Natsev Paul, Toderici George, Varadarajan Balakrishnan, Sudheendra Vijayanarasimhan, "2016. YouTube-8M: A large-scale video classification benchmark", CoRR abs/1609.08675, 2016, [online] Available: http://arxiv.org/abs/1609.08675.
ImageNet. 2017, [online] Available: http://image-Net.org.
Krizhevsky Alex, Geoffrey Hinton, Learning multiple layers of features from tiny images, vol. 1, no. 4., 2009.
David H. Hubel, Torsten N. Wiesel, "1962. Receptive fields binocular interaction and functional architecture in the cats visual cortex", Journal of Physiology, vol. 160, no. 1, pp. 106154, 1962.
Pouyanfar Samira, Sadiq Saad, Yan Yilin, Tian Haiman, Tao Yudong, Reyes Maria Presa, Shyu Mei-Ling, Chen Shu-Ching, S. S. Iyengar, "A Survey on Deep Learning: Algorithms Techniques and Applications", ACM Comput. Surv., vol. 51, no. 5, pp. 36, September 2018.
Cho Kyunghyun, Merrienboer Bart van, Gulehre Aglar, Dzmitry Bah-danau, Bougares Fethi, Schwenk Holger, Yoshua Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", The Conference on Empirical Methods in Natural Language Processing, pp. 17241734, 2014.
Li Xiangang, Wu Xihong, "Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition", IEEE International Conference on Acoustics Speech and Signal Processing, pp. 45204524, 2015.
K. He et al., "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770778, June 2016.
Goodfellow Ian, Jean Pouget-Abadie, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron, Bengio Yoshua, "Generative adversarial nets", Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2014), pp. 26722680.
Yann Le Cun, Yoshua Bengio, "Convolutional networks for images speech and time series" in The handbook of brain theory and neural networks, vol. 3361.10, no. 1995, 1995.
LeCun Yann, Bengio Yoshua, Geoffrey Hinton, "Deep learning", nature, vol. 521, no. 7553, pp. 436, 2015.
S. Narang, G. Diamos, Baidu DeepBench, 2017, [online] Available: https://github.com/baidu-research/DeepBench.
K. He et al., "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770778, June 2016.
J. Clark, Google Turning Its Lucrative Web Search Over to AI Machines. Bloomberg Technology, October 2015.
Y. Wu et al., Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, September 2016, [online] Available: http://arxiv.org/abs/1609.08144.
D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, "Mastering the game of Go with deep neural networks and tree search", Nature, vol. 529, no. 7587, 2016.
Pouyanfar Samira, Sadiq Saad, Yan Yilin, Tian Haiman, Tao Yudong, Reyes Maria Presa, Shyu Mei-Ling, Chen Shu-Ching, S. S. Iyengar, "A Survey on Deep Learning: Algorithms Techniques and Applications", ACM Comput. Surv., vol. 51, no. 5, pp. 36, September 2018.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, J. Collomosse, "Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask", 2017 30th SIBGRAPI Conference on Graphics Patterns and Images Tutorials (SIBGRAPI-T), pp. 17-41, 2017.
Karen Simonyan, Andrew Zisserman, "Networks for Large-scale Networks for Large-scale image recognition", Proc. of ICLR, 2015.
KrizhevskyIlya, A. Sutskever, E. Geoffrey, ImageNet Classification with Deep Convolutional Neural Networks December 2011Advances in neural information processing systems, vol. 25, no. 2.
John Hennessy, Patterson David, "A new golden age for computer architecture: Domain-specific hardware/software co-design en-hanced security open instruction sets and agile chip development", ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 27-29, 2018, 2018.
M. Bedford Taylor, "The Evolution of Bitcoin Hardware", Computer, vol. 50, no. 9, pp. 58-66, 2017.
C. Tan, M. Karunaratne, T. Mitra, L. Peh, "Stitch: Fusible Heterogeneous Accelerators Enmeshed with Many-Core Architecture for Wearables", 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp. 575-587, 2018.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
David B. Kirk, W. Hwu Wen-mei, "Programming Massively Parallel Processors" in A Hands-On Approach (3rd ed.), San Francisco, CA, USA:Morgan Kaufmann Publishers Inc., 2016.
Publicado
28/10/2019
Como Citar
PIAS, Marcelo; BOTELHO, Silvia; DREWS-JR, Paulo.
Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Computer Vision. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
DOI: https://doi.org/10.5753/sibgrapi.2019.9771.
