| Title |
A Method for Accelerating the Inference Process of FPGA-based LSTM for Biometric Systems |
| Authors |
(Ukyo Yoshimura) ; (Toshiyuki Inoue) ; (Akira Tsuchiya) ; (Keiji Kishine) |
| DOI |
https://doi.org/10.5573/IEIESPC.2021.10.5.416 |
| Keywords |
SoC design and applications; Neural chips; High-level synthesis; Bioinformatics; Radar and sonar signal processing |
| Abstract |
Biometric systems require the regression and classification of biological sensing data, which are both carried out using machine learning. Long short-term memory (LSTM) is one of the most common methods used for regression and classification. We have developed and implemented a low-energy LSTM algorithm for the regression of microwave sensor signals in a small-scale FPGA. Experimental results show that the FPGA-based parallel-pipelined unrolled algorithm can reduce the computation time by 95% compared to an FPGA-based sequential algorithm. In addition, we found that the power consumption can be reduced by 92% and 91% compared to that obtained with a high-end GPU and CPU, respectively. |