| Presentation | 2021-06-08 Automatic generation of executable code for ReNA Yuta Masuda, Yasuhiro Nakahara, Motoki Amagasaki, Masahiro Iida, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We have been developing ReNA as a CNN accelerator for the edge, which is controlled by directly specifying control signals for each circuit by microcode instructions. The current control method is not efficient because of its low readability and manual generation of the execution code. In addition, it requires a large amount of instructions and large SRAM size to store the control signals. In this paper, we try to solve this problem by abstracting the microcode instructions and reducing the amount of instructions. We also improve the efficiency of model implementation by enabling automatic generation of the microcode. As a result, we were able to reduce the required SRAM capacity by about 86% and halve the area of the SRAM for storing instructions. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | DeepLearning / Convolutional Neural Network / AI Chip |
| Paper # | RECONF2021-6 |
| Date of Issue | 2021-06-01 (RECONF) |
| Conference Information | |
| Committee | RECONF |
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| Conference Date | 2021/6/8(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reconfigurable system, etc. |
| Chair | Yuichiro Shibata(Nagasaki Univ.) |
| Vice Chair | Kentaro Sano(RIKEN) / Yoshiki Yamaguchi(Tsukuba Univ.) |
| Secretary | Kentaro Sano(e-trees.Japan) / Yoshiki Yamaguchi(NEC) |
| Assistant | Hiroki Nakahara(Tokyo Inst. of Tech.) / Yukitaka Takemura(INTEL) |
| Paper Information | |
| Registration To | Technical Committee on Reconfigurable Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automatic generation of executable code for ReNA |
| Sub Title (in English) | |
| Keyword(1) | DeepLearning |
| Keyword(2) | Convolutional Neural Network |
| Keyword(3) | AI Chip |
| 1st Author's Name | Yuta Masuda |
| 1st Author's Affiliation | Kumamoto University(Kumamoto Univ.) |
| 2nd Author's Name | Yasuhiro Nakahara |
| 2nd Author's Affiliation | Kumamoto University(Kumamoto Univ.) |
| 3rd Author's Name | Motoki Amagasaki |
| 3rd Author's Affiliation | Kumamoto University(Kumamoto Univ.) |
| 4th Author's Name | Masahiro Iida |
| 4th Author's Affiliation | Kumamoto University(Kumamoto Univ.) |
| Date | 2021-06-08 |
| Paper # | RECONF2021-6 |
| Volume (vol) | vol.121 |
| Number (no) | RECONF-59 |
| Page | pp.pp.26-31(RECONF), |
| #Pages | 6 |
| Date of Issue | 2021-06-01 (RECONF) |