Implementation of Low-Energy LSTM with Parallel and Pipelined Algorithm in Small-Scale FPGA

Regression and classification are necessary for biometric systems and carried out using machine learning. A method for regression and classification is long short-term memory (LSTM). We proposed and implemented algorithms for low-energy LSTM for the regression of microwave-sensor signals into a small-scale FPGA. We found that our FPGA-based parallel (including unrolled)-pipelined algorithm decreased the computation time by 95% compared with the FPGA-based sequential algorithm. In addition, the amount of energy consumption with the proposed algorithm was reduced by 92% and 91% compared with that with a high-end GPU and CPU, respectively.