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We propose a technology-independent method, referred to as adjacentconnection matrix (ACM), to efficiently map signed weight matrices tonon-negative crossbar arrays. When compared to same-hardware-overhead mappingmethods, using ACM leads to improvements of up to 20% in training accuracy forResNet-20 with the CIFAR-10 dataset when training with 5-bit precision crossbararrays or lower. When compared with strategies that use two elements torepresent a weight, ACM achieves comparable training accuracies, while alsooffering area and read energy reductions of 2.3x and 7x, respectively. ACM alsohas a mild regularization effect that improves inference accuracy in crossbararrays without any retraining or costly device/variation-aware training.

A Device Non-Ideality Resilient Approach for Mapping Neural Networks to Crossbar Arrays