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Analog Computing-in-Memory is promising and energy-efficient AI acceleration hardware due to the elimination of von-Neumann bottleneck and adoption of resistive synaptic cell (RSC) as calculation element. However, the performance of ACiM declines due to the non-idealities of RSC. In this study, a novel noise-aware training method is introduced which is aware of real distribution of array-level RSC conductances to make large-scale ACiM with a robustness. By comparing the inference accuracies under different training methods and non-idealities, the gap between ideal software accuracy and simulated ACiM inference accuracy can be reduced to 3.85% when all non-idealities are optimized along with the introduction of the proposed noise-aware training method. The simulation results can provide a standard guide for understanding how each non-ideality contributes to inference accuracy and how the neural network should be trained to optimize with the ACiM.
Kim et al. (Sun,) studied this question.