  In this work, PRUNIX, a framework for training and pruning convolutional
neural networks is proposed for deployment on memristor crossbar based
accelerators. PRUNIX takes into account the numerous non-ideal effects of
memristor crossbars including weight quantization, state-drift, aging and
stuck-at-faults. PRUNIX utilises a novel Group Sawtooth Regularization intended
to improve non-ideality tolerance as well as sparsity, and a novel Adaptive
Pruning Algorithm (APA) intended to minimise accuracy loss by considering the
sensitivity of different layers of a CNN to pruning. We compare our
regularization and pruning methods with other standards on multiple CNN
architectures, and observe an improvement of 13% test accuracy when
quantization and other non-ideal effects are accounted for with an overall
sparsity of 85%, which is similar to other methods
