publication . Preprint . Conference object . 2018

EVA 2 : exploiting temporal redundancy in live computer vision

Mark Buckler; Philip Bedoukian; Suren Jayasuriya; Adrian Sampson;
Open Access English
  • Published: 16 Mar 2018
Abstract
Comment: Appears in ISCA 2018
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
64 references, page 1 of 5

[1] Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam, “ShiDianNao: Shifting vision processing closer to the sensor,” in International Symposium on Computer Architecture (ISCA), 2015. [OpenAIRE]

[2] Y.-H. Chen, T. Krishna, J. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” in IEEE International Solid-State Circuits Conference (ISSCC), 2016.

[3] A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. Emer, S. W. Keckler, and W. J. Dally, “SCNN: An accelerator for compressed-sparse convolutional neural networks,” in International Symposium on Computer Architecture (ISCA), 2017. [OpenAIRE]

[4] J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in International Symposium on Computer Architecture (ISCA), 2016. [OpenAIRE]

[5] S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” in International Conference on Learning Representations (ICLR), 2016.

[6] S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “EIE: Efficient inference engine on compressed deep neural network,” in International Symposium on Computer Architecture (ISCA), 2016.

[7] U. A. Acar, Self-adjusting Computation. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2005.

[8] M. A. Hammer, K. Y. Phang, M. Hicks, and J. S. Foster, “Adapton: Composable, demand-driven incremental computation,” in ACM Conference on Programming Language Design and Implementation (PLDI), 2014.

[9] M. Samadi, J. Lee, D. A. Jamshidi, A. Hormati, and S. Mahlke, “SAGE: Self-tuning approximation for graphics engines,” in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2013.

[10] M. Ringenberg, A. Sampson, I. Ackerman, L. Ceze, and D. Grossman, “Monitoring and debugging the quality of results in approximate programs.,” in International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2015.

[11] D. S. Khudia, B. Zamirai, M. Samadi, and S. Mahlke, “Rumba: An online quality management system for approximate computing,” in International Symposium on Computer Architecture (ISCA), 2015. [OpenAIRE]

[12] Y.-W. Huang, C.-Y. Chen, C.-H. Tsai, C.-F. Shen, and L.-G. Chen, “Survey on block matching motion estimation algorithms and architectures with new results,” Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, vol. 42, pp. 297-320, Mar. 2006.

[13] X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei, “Deep feature flow for video recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[14] X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei, “Flow-guided feature aggregation for video object detection,” in IEEE International Conference on Computer Vision (ICCV), 2017.

[15] P. O'Connor and M. Welling, “Sigma delta quantized networks,” in International Conference on Learning Representations (ICLR), 2017.

64 references, page 1 of 5
Abstract
Comment: Appears in ISCA 2018
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
64 references, page 1 of 5

[1] Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam, “ShiDianNao: Shifting vision processing closer to the sensor,” in International Symposium on Computer Architecture (ISCA), 2015. [OpenAIRE]

[2] Y.-H. Chen, T. Krishna, J. Emer, and V. Sze, “Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks,” in IEEE International Solid-State Circuits Conference (ISSCC), 2016.

[3] A. Parashar, M. Rhu, A. Mukkara, A. Puglielli, R. Venkatesan, B. Khailany, J. Emer, S. W. Keckler, and W. J. Dally, “SCNN: An accelerator for compressed-sparse convolutional neural networks,” in International Symposium on Computer Architecture (ISCA), 2017. [OpenAIRE]

[4] J. Albericio, P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos, “Cnvlutin: Ineffectual-neuron-free deep neural network computing,” in International Symposium on Computer Architecture (ISCA), 2016. [OpenAIRE]

[5] S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” in International Conference on Learning Representations (ICLR), 2016.

[6] S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally, “EIE: Efficient inference engine on compressed deep neural network,” in International Symposium on Computer Architecture (ISCA), 2016.

[7] U. A. Acar, Self-adjusting Computation. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, 2005.

[8] M. A. Hammer, K. Y. Phang, M. Hicks, and J. S. Foster, “Adapton: Composable, demand-driven incremental computation,” in ACM Conference on Programming Language Design and Implementation (PLDI), 2014.

[9] M. Samadi, J. Lee, D. A. Jamshidi, A. Hormati, and S. Mahlke, “SAGE: Self-tuning approximation for graphics engines,” in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2013.

[10] M. Ringenberg, A. Sampson, I. Ackerman, L. Ceze, and D. Grossman, “Monitoring and debugging the quality of results in approximate programs.,” in International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2015.

[11] D. S. Khudia, B. Zamirai, M. Samadi, and S. Mahlke, “Rumba: An online quality management system for approximate computing,” in International Symposium on Computer Architecture (ISCA), 2015. [OpenAIRE]

[12] Y.-W. Huang, C.-Y. Chen, C.-H. Tsai, C.-F. Shen, and L.-G. Chen, “Survey on block matching motion estimation algorithms and architectures with new results,” Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, vol. 42, pp. 297-320, Mar. 2006.

[13] X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei, “Deep feature flow for video recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[14] X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei, “Flow-guided feature aggregation for video object detection,” in IEEE International Conference on Computer Vision (ICCV), 2017.

[15] P. O'Connor and M. Welling, “Sigma delta quantized networks,” in International Conference on Learning Representations (ICLR), 2017.

64 references, page 1 of 5
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