Robustness of a memristor based liquid state machine

The ability to learn from noisy and incomplete information is highly desired in cognitive systems. When these cognitive systems are realized in hardware, such as neuromemristive systems, an added constraint is how the algorithms adapt to the inherent noise and variability from the devices. In this work, we explore the robustness of the reservoir computing algorithm, specifically a liquid state machine, when realized as a mixed signal neuromemristive system. The study focuses on robustness of the liquid state machine under different manifestations of memristor read and write noise. A high-level analysis on the liquid state machine's accuracy for simultaneous occurrence of multiple sources of noise is also investigated. For analysis, TIMIT speech recognition and spoken arabic digit datasets were used. The results support that the neuromemristive liquid state machine has high immunity to a variety of non-ideal device effects. There is 22% degradation in the classification accuracy of the Liquid state machine, even in cases where 15% of the neurons in the liquid are faulty.