Neural networks are widely used in applications such as machine translation, speech recognition, etc. Among the different types of neural networks, recurrent neural networks (RNN) based on the Long Short-Term Memory (LSTM) architecture have become popular for elaborating time series. To improve accuracy, the size of LSTM models continues to grow. Matrix-vector multiplications (MxV) are the most computation-intensive and time-consuming operations involved in LSTM inference. In order to perform these operations with high performance and low power consumption, Field-Programmable Gate Arrays (FPGAs) have become popular to accelerate LSTM inference. Based on FPGAs, finding the best accelerator architecture for a given objective and combining the algorithm-level optimizations become the hot issues.