  An adaptive inference method for crossbar (AIDX) is presented based on an
optimization scheme for adjusting the duration and amplitude of input voltage
pulses. AIDX minimizes the long-term effects of memristance drift on artificial
neural network accuracy. The sub-threshold behavior of memristor has been
modeled and verified by comparing with fabricated device data. The proposed
method has been evaluated by testing on different network structures and
applications, e.g., image reconstruction and classification tasks. The results
showed an average of 60% improvement in convolutional neural network (CNN)
performance on CIFAR10 dataset after 10000 inference operations as well as
78.6% error reduction in image reconstruction.
