RRAM based convolutional neural networks for high accuracy pattern recognition and online learning tasks

In this work, we conduct research on optimizing schemes for the RRAM-based implementation of CNN. Our main achievements contain: 1) A concrete CNN circuit and corresponding operation methods are developed. 2) Quantification methods for utilizing binary or multilevel RRAM as synapses are proposed, and our CNN performs with 98% accuracy on the MNIST dataset using multilevel RRAM and 97% accuracy using binary RRAM. 3) Influence of the number and size of kernels, as well as the device conductance variation on final recognition accuracy is studied in detail. 4) Online learning tasks are performed using the developed CNN system with binary STDP protocol, and 94.6% accuracy on average is achieved.