Resource-efficient streaming architecture for ensemble Kalman filters designed for online learning in physical reservoir computing

In edge computing, data are processed on the device side in advance, often by means of reservoir computing. Ensemble Kalman filters can be used to improve the learning processes of reservoir computing methods. In this study, we designed and validated an architecture for this approach, where we implemented techniques such as parallel computation by initiating streaming processes, reducing dividers, and accumulating random numbers. The validation results demonstrate that the proposed architecture reduces the time and resource costs of computation while maintaining a sufficient estimation accuracy. These results may facilitate the implementation of AI methods on a small scale.