DefectTolerant and EnergyEfficient Training of MultiValued and Binary Memristor Crossbars for NearSensor Cognitive Computing Nearsensor cognitive computing can reduce an amount of data to be delivered to the server significantly. A memristorCMOS hybrid circuit can be integrated with IoT sensors, for the nearsensor cognitive computing. One problem of the real memristor crossbar is memristor defects. In this paper, we discuss the chipintheloop learning to consider the memristor defects in the crossbar. For energyefficient and defecttolerant training of the crossbar, we explain the partialgated training for the multivalued memristors. Moreover, for binarymemristor neural networks, we show the asymmetrical programming scheme of the combination of coarseHRS and fineLRS can save the programming time and energy much than the highprecision programming of the entire crossbar.

DefectTolerant Crossbar Training of Memristor Ternary Neural Networks A memristor Ternary Neural Network (TNN) is a promising candidate for implementing neural networks for InternetofThings (IoT) applications, where low power and simple hardware are important. One important concern in the memristor TNN is that the real memristor crossbar has various defects such as stuckatfaults, memristance variation, etc., like human brainu0027s biological neurons and synapses. To mitigate the inference loss due to the memristive defects, we need to retrain the defective crossbar. However, the crossbaru0027s retraining needs a long time and a large amount of energy of programming, because the memristors should be programmed one by one using Incremental Step Pulse Programming (ISPP) of flash memories. Here, we combine the partialgated training scheme with the asymmetrical training for not only minimizing the recognition rate loss, but also saving the crossbaru0027s programming time and energy. The CF scheme with 10% retraining indicates the programming energy can be saved by as large as xe2x88xbc98%, in sacrifice with the MNIST rate loss of xe2x88xbc0.6%, compared to the FF scheme with 100% retraining. The simulation indicates that the CF with 10% retraining will be useful for realizing the crossbarbased neuralnetwork in large scale for future IoT applications.