Name
Title | ||
|---|---|---|
Energy-Efficient Ecg Signals Outlier Detection Hardware Using A Sparse Robust Deep Autoencoder |
Abstract | ||
|---|---|---|
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. A mobile ECG analysis device is needed so that abnormal ECG waves can be detected anywhere. Such mobile device requires a real-time performance and low power consumption, 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, and all the parameters are converted into fixed-point values. We show that even if the parameters are reduced converted into fixed-point values, the outlier detection performance degradation is only 0.83 points. By reducing the volume of the weight parameters, all the parameters can be stored in on-chip memory. We design the architecture according to the CRS format, which is the well-known data structure of a sparse matrix, minimizing the hardware size and reducing the power consumption. We use weight sharing to further reduce the weight-parameter volumes. By using weight sharing, we could reduce the bit width of the memories by 60% while maintaining the outlier detection performance. 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1587/transinf.2020LOP0011 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | DocType | Volume |
FPGA, autoencoder, outlier detection, unsupervised training | Journal | E104D |
Issue | ISSN | Citations |
8 | 1745-1361 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N Soga | 1 | 0 | 0.34 |
| S Sato | 2 | 0 | 0.34 |
| Hiroki Nakahara | 3 | 155 | 37.34 |