Device quantization policy in variation-aware in-memory computing design.

Device quantization of in-memory computing (IMC) that considers the non-negligible variation and finite dynamic range of practical memory technology is investigated, aiming for quantitatively co-optimizing system performance on accuracy, power, and area. Architecture- and algorithm-level solutions are taken into consideration. Weight-separate mapping, VGG-like algorithm, multiple cells per weight, and fine-tuning of the classifier layer are effective for suppressing inference accuracy loss due to variation and allow for the lowest possible weight precision to improve area and energy efficiency.