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Recently, recurrent neural network, namely RNN, is used to mitigate the nonlinear impairment of channel, and achieve good performance. However, the training process of RNN is always time-consuming and inherently difficult though with high performance computing devices like field programmable gate arrays (FPGAs) and graphics processing units (GPUs), which may hinder it application in on site use. Reservoir computing, namely RC, is a novel computational framework, which can be regarded as a special RNN. RC consists of three layers: input layer, reservoir layer, and output layer. Different from traditional RNN, the weights between the input layer and reservoir layer, and the weights inside the reservoir layer are fixed. Only the weights between the reservoir layer and the output layer need to be trained in the training process, which greatly reduce the difficulty and time cost of training phase. More importantly, RC has achieved the state of the art performance for many temporal benchmark tasks. |
IEEE/ICACT20210147 Slide.05
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