A Decomposable Winograd Method for N–D Convolution Acceleration in Video Analysis

Author:

Huang Di,Zhang RuiORCID,Zhang Xishan,Wu Fan,Wang Xianzhuo,Jin Pengwei,Liu Shaoli,Li Ling,Chen Yunji

Funder

beijing nova program of science and technology

beijing natural science foundation

nsf of china

strategic priority research program of chinese academy of science

beijing academy of artificial intelligence

youth innovation promotion association of the chinese academy of sciences

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Reference62 articles.

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2. Barabasz, B,, & Gregg, D. (2019). Winograd convolution for dnns: Beyond linear polinomials. arXiv:1905.05233.

3. Budden, D., Matveev, A., Santurkar, S., Chaudhuri, S. R., & Shavit, N. (2017) Deep tensor convolution on multicores. In Proceedings of the 34th international conference on machine learning-volume 70, JMLR. org (pp. 615–624).

4. Butler, D., Wulff, J., Stanley, G., & Black, M. J. (2012). A naturalistic open source movie for optical flow evaluation. In ECCV

5. Cai, H., Zhu, L., & Han, S. (2018). Proxylessnas: Direct neural architecture search on target task and hardware. arXiv:1812.00332.

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