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The Impact of Endurance Degradation in Analog RRAM for In-Situ Training

The analog resistive random access memory (RRAM) is attractive for neuromorphic computing systems as synaptic weights. However, cycling endurance is one of the most important limitations to reliability. In this work, two endurance degradation models for analog RRAM are proposed based on the measurement results. Then, the models are utilized to investigate the impact on in-situ training accuracy. We use the MNIST dataset trained in a 3-layer multilayer perceptron (MLP), with the cycling endurance of RRAM degrading. The results indicate that endurance degradation will decrease the in-situ training accuracy.