||A Method for Accelerating the Inference Process of FPGA-based LSTM for Biometric Systems
||(Ukyo Yoshimura) ; (Toshiyuki Inoue) ; (Akira Tsuchiya) ; (Keiji Kishine)
|| SoC design and applications; Neural chips; High-level synthesis; Bioinformatics; Radar and sonar signal processing
||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.