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DNNs deployed on analog processing in memory (PIM) architectures are subjectto fabrication-time variability. We developed a new joint variability- andquantization-aware DNN training algorithm for highly quantized analog PIM-basedmodels that is significantly more effective than prior work. It outperformsvariability-oblivious and post-training quantized models on multiple computervision datasets/models. For low-bitwidth models and high variation, the gain inaccuracy is up to 35.7% for ResNet-18 over the best alternative.  We demonstrate that, under a realistic pattern of within- and between-chipcomponents of variability, training alone is unable to prevent large DNNaccuracy loss (of up to 54% on CIFAR-100/ResNet-18). We introduce a self-tuningDNN architecture that dynamically adjusts layer-wise activations duringinference and is effective in reducing accuracy loss to below 10%.

Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM