Keywords:Reservoir computing, Single walled carbon nanotube, Porphyrin polyoxometalate
Reservoir computing (RC) has become a core deep neural network algortihm for learning and training big complex data in an energy-efficient way. Among various other physical reservoir systems already reported we here exploit the dynamics of single-waaled carbon nanitube/porphyrin polyoxometalate (SWNT/Por-POM) by conducting electrical measurements. Properties like non-linearity, inverse power law scaling, fading memory, high dimensionality and phase delayed non-linear outputs (echo-state property) from a time varying sine wave input satisfies the RC charecteristics. The benchmark RC task of waveform generation and non-linear autoregressive moving average (NARMA) time prediction series were also successfully achieved. Based on these findings we conclude SWNT/Por-POM can solve cognitive tasks in real time and can be used in the future for speech recognition as well.