| Presentation | 2020-11-17 Energy-Efficient ECG Signals Outlier Detection Hardware Using a Sparse Robust Deep Autoencoder Naoto Soga, Shimpei Sato, HIroki Nakahara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Advancements in portable electrocardiographs have allowed electrocardiogram (ECG) signals to be recorded in everyday life. Machine-learning techniques, including deep learning, have been used in numerous studies to analyze ECG signals because they exhibit superior performance to conventional methods. However, deep-learning based models often have too many parameters to implement on mobile hardware, its amount of hardware is too large and dissipates much power consumption. We propose a design flow to implement the outlier detector using an autoencoder on a low-end FPGA. To shorten the preparation time of ECG data used in training an autoencoder, an unsupervised learning technique is applied. Additionally, to minimize the volume of the weight parameters, a weight sparseness technique is applied. Also, all the parameters are converted into fixed-point values, and weight sharing technique was applied to further reduce the weight parameter volume. We implemented the autoencoder on a Digilent Inc. ZedBoard and compared the results with those for the ARM mobile CPU for a built-in device. The results indicated that our FPGA implementation of the outlier detector was 12 times faster and 106 times more energy-efficient. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | outlier detection / autoencoder / unsupervised learning / FPGA |
| Paper # | VLD2020-17,ICD2020-37,DC2020-37,RECONF2020-36 |
| Date of Issue | 2020-11-10 (VLD, ICD, DC, RECONF) |
| Conference Information | |
| Committee | VLD / DC / RECONF / ICD / IPSJ-SLDM |
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| Conference Date | 2020/11/17(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Gaia 2020 -New Field of VLSI Design- |
| Chair | Daisuke Fukuda(Fujitsu Labs.) / Hiroshi Takahashi(Ehime Univ.) / Yuichiro Shibata(Nagasaki Univ.) / Makoto Nagata(Kobe Univ.) / Yuichi Nakamura(NEC) |
| Vice Chair | Kazutoshi Kobayashi(Kyoto Inst. of Tech.) / Tatsuhiro Tsuchiya(Osaka Univ.) / Kentaro Sano(RIKEN) / Yoshiki Yamaguchi(Tsukuba Univ.) / Masafumi Takahashi(masafumi2.takahashi@kioxia.com) |
| Secretary | Kazutoshi Kobayashi(Hitachi) / Tatsuhiro Tsuchiya(Osaka Univ.) / Kentaro Sano(Nihon Univ.) / Yoshiki Yamaguchi(Chiba Univ.) / Masafumi Takahashi(e-trees.Japan) / (NEC) |
| Assistant | Takuma Nishimoto(Hitachi) / / Hiroki Nakahara(Tokyo Inst. of Tech.) / Yukitaka Takemura(INTEL) / Koji Nii(TSMC) / Kosuke Miyaji(Shinshu Univ.) / Takeshi Kuboki(Kyushu Univ.) |
| Paper Information | |
| Registration To | Technical Committee on VLSI Design Technologies / Technical Committee on Dependable Computing / Technical Committee on Reconfigurable Systems / Technical Committee on Integrated Circuits and Devices / Special Interest Group on System and LSI Design Methodology |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Energy-Efficient ECG Signals Outlier Detection Hardware Using a Sparse Robust Deep Autoencoder |
| Sub Title (in English) | |
| Keyword(1) | outlier detection |
| Keyword(2) | autoencoder |
| Keyword(3) | unsupervised learning |
| Keyword(4) | FPGA |
| 1st Author's Name | Naoto Soga |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 2nd Author's Name | Shimpei Sato |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 3rd Author's Name | HIroki Nakahara |
| 3rd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| Date | 2020-11-17 |
| Paper # | VLD2020-17,ICD2020-37,DC2020-37,RECONF2020-36 |
| Volume (vol) | vol.120 |
| Number (no) | VLD-234,ICD-235,DC-236,RECONF-237 |
| Page | pp.pp.36-41(VLD), pp.36-41(ICD), pp.36-41(DC), pp.36-41(RECONF), |
| #Pages | 6 |
| Date of Issue | 2020-11-10 (VLD, ICD, DC, RECONF) |