Reservoir computing is a computational model capable of processing simple time varying, time-series data, tasks with low power consumption and high speed operation. A concept that contrasts with reservoir computing is the deep learning model. With the development of AI and the use of big data in recent years, the challenges of computational processing of huge amounts of data and increasing power consumption have become apparent, and the rapid spread of generative AI has made AI processing increasingly dependent on the cloud. Traditional deep learning models consist of an input layer, a hidden layer, and an output layer. The input layer receives the information first, and the hidden layer performs various and huge number calculations. The final output layer shows the learning results. The more hidden layers there are, the more complex computations (~trillions) can be performed. However, this leads to massive data processing, resulting in increased power consumption and latency.
