| Presentation | 2021-03-03 Energy Efficient Approximate Storing to MRAM for Deep Neural Network Tasks in Edge Computing Yoshinori Ono, Kimiyoshi Usami, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | On-chip learning is gaining attention in edge devices. In addition, a magnetic RAM (MRAM) is a promising memory technology for edge devices because of low leakage energy. However, the high write energy is a disadvantage of MRAM. For minimizing the write energy, we propose an approximate storing approach to MRAM for learning tasks of deep neural networks (DNN). The proposed approach writes the weight and bias data to MRAM approximately on each epoch with the fine-grained adjusted write time. Simulation results with image recognition DNN applications have demonstrated that the write energy can be reduced in the range from 9% to 37% with negligible (< 0.5%) accuracy loss. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | MRAM / Energy Minimization / Approximate Computing / Deep Learning / On-chip Learning |
| Paper # | VLD2020-67,HWS2020-42 |
| Date of Issue | 2021-02-24 (VLD, HWS) |
| Conference Information | |
| Committee | HWS / VLD |
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| Conference Date | 2021/3/3(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Technology for System-on-Silicon, Hardware Security, etc. |
| Chair | Makoto Ikeda(Univ. of Tokyo) / Daisuke Fukuda(Fujitsu Labs.) |
| Vice Chair | Yasuhisa Shimazaki(Renesas Electronics) / Makoto Nagata(Kobe Univ.) / Kazutoshi Kobayashi(Kyoto Inst. of Tech.) |
| Secretary | Yasuhisa Shimazaki(Kyushu Univ.) / Makoto Nagata(NTT) / Kazutoshi Kobayashi(Hitachi) |
| Assistant | / Takuma Nishimoto(Hitachi) |
| 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) | Energy Efficient Approximate Storing to MRAM for Deep Neural Network Tasks in Edge Computing |
| Sub Title (in English) | |
| Keyword(1) | MRAM |
| Keyword(2) | Energy Minimization |
| Keyword(3) | Approximate Computing |
| Keyword(4) | Deep Learning |
| Keyword(5) | On-chip Learning |
| 1st Author's Name | Yoshinori Ono |
| 1st Author's Affiliation | Shibaura Institute of Technology(SIT) |
| 2nd Author's Name | Kimiyoshi Usami |
| 2nd Author's Affiliation | Shibaura Institute of Technology(SIT) |
| Date | 2021-03-03 |
| Paper # | VLD2020-67,HWS2020-42 |
| Volume (vol) | vol.120 |
| Number (no) | VLD-400,HWS-401 |
| Page | pp.pp.1-6(VLD), pp.1-6(HWS), |
| #Pages | 6 |
| Date of Issue | 2021-02-24 (VLD, HWS) |