This chapter focuses on the LSTM model and is concerned with the design of a high-performance and energy-efficient solution to implement deep learning inference. The chapter is organized as follows: Section 2.1 introduces Recurrent Neural Networks (RNNs). In this section Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) network models are discussed as special kind of RNNs. Section 2.2 discusses inference acceleration with hardware. In Section 2.3, a survey on various FPGA designs is presented within the context of the results of previous related works and after which Section 2.4 concludes the chapter.