Fig. 2: RC system architecture and waveform classification demonstration.

a Schematic of a conventional RC system. The input is fed to a reservoir, which is composed by a large number of nonlinear nodes. The internal connections among these nodes are random and fixed. The correct output learns from the states of nodes by training the output weights. b Schematic of the dynamic memristor-based RC system. For a given input, the input vector is transformed into a temporal signal through a mask (that is the time multiplexing process) and then fed to the reservoir, which consists of a dynamic memristor and a load resistor in series. The memristor responses within a duration time τ are selected as the virtual nodes with a fixed time step δ. The output vector is a linear combination of the values in the virtual nodes and the weights (W_out) can be trained through linear regression. c Schematic of a dynamic memristor-based parallel RC system, where the mask sequences are different for every single memristor RC unit. The output is the linear combination of all reservoir states. In our experiment, this parallel RC system is realized by testing single memristor in multiple cycles. d The input and classification result of sine and square waves. The input sequence is a random combination of sine and square waveforms, where the sampling points for each waveform are set to 8. The optimal classification results are achieved when the length of mask sequence and the number of reservoirs in parallel are set to 4 and 10, respectively, and the lowest NRMSE we get is 0.14. e NRMSE changes with the mask length when keeping the reservoir size (that is the product of mask length M and number of reservoirs N) the same. Ten different devices are tested and the average of NRMSE reaches the minimum value as the mask length reaches 4. The error bar shows the variation between devices.