Jumping Memory for Memristive Reservoir Computing

Reservoir computing offers a hardware-friendly machine learning approach that minimizes connectivity overhead and adapts well to various dynamic systems. However, traditional implementations suffer from limited computational capacity due to fading memory effects. This study introduces a novel memristive reservoir that exploits on the nonlinear properties of dynamic memristors and incorporates jumping memory to enhance its computational capabilities. By utilizing jumping memory technology, this model enhances the system’s ability to better handle time series tasks, effectively overcoming the memory capacity limitations of traditional memristive reservoir computing. Our experiments show that this approach demonstrates superior performance in system identification task and time series prediction tasks compared with the six SOTA approaches.