| Presentation | 2019-02-28 Selection of Gaussian Mixture Reduction Methods Using Machine Learning Haruki Kazama, Shuji Tsukiyama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Gaussian mixture model is a useful distribution for statistical methods such as statistical static timing analysis, but the number of components of Gaussian mixture model increases exponentially by statistical operations. Hence, the number of components must be reduced to around 2 in order to repeat operations effectively and efficiently. Although several methods for reducing the number of components have been proposed, each of them has strength and weakness in accuracy and time complexity. Therefore, selecting an appropriate reduction method for an input distribution is a practical way for reducing the number of components. This paper proposes a selection method using machine learning and evaluates its performance. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Gaussian mixture model / Gaussian reduction / method selection / support vector machine / experimental results |
| Paper # | VLD2018-113,HWS2018-76 |
| Date of Issue | 2019-02-20 (VLD, HWS) |
| Conference Information | |
| Committee | HWS / VLD |
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| Conference Date | 2019/2/27(4days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Ken Seinen Kaikan |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Technology for System-on-Silicon, Hardware Security, etc. |
| Chair | Tsutomu Matsumoto(Yokohama National Univ.) / Noriyuki Minegishi(Mitsubishi Electric) |
| Vice Chair | Shinichi Kawamura(Toshiba) / Makoto Ikeda(Univ. of Tokyo) / Nozomu Togawa(Waseda Univ.) |
| Secretary | Shinichi Kawamura(Kobe Univ.) / Makoto Ikeda(SECOM) / Nozomu Togawa(NTT) |
| Assistant | |
| Paper Information | |
| Registration To | Technical Committee on Hardware Security / Technical Committee on VLSI Design Technologies |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Selection of Gaussian Mixture Reduction Methods Using Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | Gaussian mixture model |
| Keyword(2) | Gaussian reduction |
| Keyword(3) | method selection |
| Keyword(4) | support vector machine |
| Keyword(5) | experimental results |
| 1st Author's Name | Haruki Kazama |
| 1st Author's Affiliation | Chuo University(Chuo Univ.) |
| 2nd Author's Name | Shuji Tsukiyama |
| 2nd Author's Affiliation | Chuo University(Chuo Univ.) |
| Date | 2019-02-28 |
| Paper # | VLD2018-113,HWS2018-76 |
| Volume (vol) | vol.118 |
| Number (no) | VLD-457,HWS-458 |
| Page | pp.pp.121-126(VLD), pp.121-126(HWS), |
| #Pages | 6 |
| Date of Issue | 2019-02-20 (VLD, HWS) |