Efficient next-generation reservoir computing: An analog in-memory implementation using memristor crossbar arrays

Reservoir computing has garnered significant attention for its efficiency in processing temporal signals, while the proposed next-generation reservoir computing (NG-RC) further enhances computational efficiency. The analog in-memory computing architecture fundamentally reduces the data transfer operations between data processing units and storage units. This study proposes a novel NG-RC implementation scheme based on memristor crossbar arrays. Specifically, the nonlinear feature vector operation is mathematically converted into matrix multiplication, enabling <i>in situ</i> computation within memristor crossbar arrays. The unique monomial of the vector outer production can be extracted by reading the current of the specific column line in array, facilitating efficient NG-RC implementation. Under ideal memristor device conditions, the proposed scheme demonstrates excellent performance in the prediction and inference tasks, validating its feasibility. Additionally, simulation experiments considering non-ideal memristor characteristics reveal that the programming error is the critical factor affecting the system performance. However, by selecting the appropriate memristor device, the proposed NG-RC implementation scheme shows comparable performance to digital implementations, confirming the effectiveness of the proposed scheme.