PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators

• URL: http://arxiv.org/abs/2202.01758v1
• Date: Thu, 3 Feb 2022 18:32:03 GMT
• Title: PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators
• Authors: Ali Alshaarawy, Amirali Amirsoleimani, Roman Genov
• Abstract summary: PRUNIX is a framework for training and pruning convolutional neural networks. It is proposed for deployment on memristor crossbar based accelerators.
• Score: 0.36832029288386126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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