Nano-ionic Solid Electrolyte FET-Based Reservoir Computing for Efficient Temporal Data Classification and Forecasting.

Physical dynamic reservoirs are well-suited for edge systems, as they can efficiently process temporal input at a low training cost by utilizing the short-term memory of the device for in-memory computation. However, the short-term memory of two-terminal memristor-based reservoirs limits the duration of the temporal inputs, resulting in more reservoir outputs per sample for classification. Additionally, forecasting requires multiple devices (20-25) for the prediction of a single time step, and long-term forecasting requires the reintroduction of forecasted data as new input, increasing system complexity and costs.