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Publication List - Rio Yokota 2017 (16 / 183 entries)
Journal Paper
International Conference (Reviewed)
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Kazuki Oosawa,
Rio Yokota.
Evaluating the Compression Efficiency of the Filters in Convolutional Neural Networks,
The 26th International Conference on Artificial Neural Networks,
Sept. 2017.
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Mustafa AbdulJabbar,
Mohammed Al Farhan,
Rio Yokota,
David Keyes.
Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture,
3rd International European Conference on Parallel and Distributed Computing,
Aug. 2017.
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Kazuki Oosawa,
Akira Sekiya,
Hiroki Naganuma,
Rio Yokota.
Accelerating Matrix Multiplication in Deep Learning by Using Low-Rank Approximation,
The 2017 International Conference on High Performance Computing & Simulation,
July 2017.
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Mustafa AbdulJabbar,
George Markomanolis,
Huda Ibeid,
Rio Yokota,
David Keyes.
Communication Reducing Algorithms for Distributed Heirarchical N-Body Methods,
32nd International Conference, ISC High Performance,
Lecture Notes in Computer Science,
Vol. 10266,
pp. 79--96,
June 2017.
Domestic Conference (Reviewed)
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Hiroki Naganuma,
Rio Yokota.
Verification of Low-precision Arithmetic for the Acceleration of Convolutional Neural Networks,
GTC Japan,
Dec. 2017.
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K. Osawa,
A. Sekiya,
H. Naganuma,
R. Yokota.
Acceleration of Convolutional Neural Networks Using Low-Rank Tensor Decomposition,
Pattern Recognition and Media Understanding,
Oct. 2017.
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H. Naganuma,
A. Sekiya,
K. Osawa,
H. Otomo,
Y. Kuwamura,
R. Yokota.
Evaluating the Performance of Deep Learning with Low Precision Arithmetic,
Pattern Recognition and Media Understanding,
Oct. 2017.
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H. Naganuma,
K. Osawa,
A. Sekiya,
R. Yokota.
Acceleration of Compressed Models in Deep Learning Using Half Precision Arithmetic,
Japan Society for Industrial and Applied Mathematics Annual Meeting,
Sept. 2017.
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Satoshi Ohshima,
Ichitaro Yamazaki,
Akihiro Ida,
Rio Yokota.
Optimization of Hierarchical Matrix Computations on a Cluster of GPUs,
Summer United Workshops on Parallel, Distributed and Cooperative Processing,
July 2017.
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Kazuki Oosawa,
Akira Sekiya,
Hiroki Naganuma,
Rio Yokota.
Accelerating Convolutional Neural Networks Using Low-Rank Approximation,
22nd Conference of Japan Computational Engineering Society,
Proceedings of the 22nd Conference of Japan Computational Engineering Society,
May 2017.
International Conference (Not reviewed / Unknown)
Domestic Conference (Not reviewed / Unknown)
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Yoshifumi Motoyama,
Toshio Endo,
SATOSHI MATSUOKA,
Rio Yokota,
Keisuke Fukuda,
佐藤 育郎.
Using Low-Rank Approximation in Convolutional Neural Networks,
158th Research Presentation Seminar in High Performance Computing,
2017-HPC-158 No.25,
Mar. 2017.
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Akira Sekiya,
Kazuki Oosawa,
Hiroki Naganuma,
Rio Yokota.
Acceleration of Matrix Multiplication in Deep Learning Using Low-Rank Approximation,
158th Research Presentation Seminar in High Performance Computing,
Mar. 2017.
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