Reservoir computing based on a solid electrolyte ZnO TFT: an attractive platform for flexible edge computing

Implementation of accurate neural network models in edge applications such as wearables is limited by the hardware platform due to constraints of power/area. We highlight novel concepts in reservoir computing that rely on a volatile three terminal solid electrolyte thin film synaptic transistor, whose conductance can be controlled by the gate and drain voltages to enhance the richness of the reservoir and operate in the off-state. The proposed approach achieves an accuracy of 94% in image processing, significantly higher than equivalent applications of reservoir computing based on two-Terminal memristors, primarily because we avoid down-sampling by training the readout after every pulse.