Reliable and robust RRAM-based neuromorphic computing

RRAM-based crossbars are a promising hardware platform to accelerate computations in neural networks. Before such a crossbar can be used as an accelerator for neural networks, RRAM cells should be programmed to target resistances to represent weights in neural networks. However, this process degrades the valid range of the resistances of RRAM cells from the fresh state, called aging effect. Therefore, after a certain number of programming iterations, these RRAM cells cannot be programmed reliably anymore, affecting the classification accuracy of neural networks negatively. In addition, process variations during manufacturing and noise during programming of RRAM cells also lead to significant accuracy degradation. To solve the problems described above, in this paper, we introduce a software/hardware codesign framework to reduce the aging effect in RRAM crossbars. To counter process variations and noise, we first model them as random variables and then modify the computations in software training considering these variables. Simulation results show that the lifetime of RRAM crossbars can be extended by up to 11 times with the codesign framework and the mean value and the standard deviation of the inference accuracy under process variations and noise can be improved significantly.