Abstract
The requirement of multilevel cell (MLC) resistive random access memory (RRAM) for computing is different than that for MLC storage. It generally requires a linearly spaced conductance median and an ultratight conductance distribution, as the column current are summed up for analog computation. In this article, 3-bit per cell RRAM that is suitable for accurate inference of a deep neural network (DNN) is demonstrated, with ultratight conductance distribution ( $5.3 \times $ and $4.4 \times $ , respectively, compared to the 3-bit per cell RRAM used as MLC storage.
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