Wafer-Scale TaOx Device Variability and Implications for Neuromorphic Computing Applications

Scaling arrays of non-volatile memory devices from academic demonstrations to reliable, manufacturable systems requires a better understanding of variability at array and wafer-scale levels. CrossSim models the accuracy of neural networks implemented on an analog resistive memory accelerator using the cycle-to-cycle variability of a single device. In this work, we extend this modeling tool to account for device-to-device variation in a realistic way, and evaluate the impact of this reliability issue in the context of neuromorphic online learning tasks.