Reservoir computing is a simple and effective machine learning algorithm to process time dependent signals. Compared with the software implementation in traditional electronic computer, reservoir computing implementation with optical components is more beneficial to information processing with ultrafast speed and ultralow power consumption. The basic principles of reservoir computing are presented, and the research progress in hardware implementation of reservoir computers is introduced from three aspects of input layer, reservoir and output layer. The existing problems in the development of the hardware implementation are demonstrated, and their future developing trends are discussed as well.