# Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks

arXiv: https://arxiv.org/abs/2201.05229 · HTML: https://mlsanity.com/paper/2201.05229/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Proving the Lottery Ticket Hypothesis: Pruning is All You Need](https://mlsanity.com/paper/2002.00585/) (2020)
- [RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars](https://mlsanity.com/paper/1809.00072/) (2018)
- [Crossbar-aware neural network pruning](https://mlsanity.com/paper/1807.10816/) (2018)
- [Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks](https://mlsanity.com/paper/2105.04003/) (2021)

## Built on by (most cited first, 5 of 5)

- [ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation](https://mlsanity.com/paper/2402.02586/) (2024)
- [XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars](https://mlsanity.com/paper/2302.07769/) (2023)
- [FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks](https://mlsanity.com/paper/2301.00675/) (2022)
- [Memory Faults in Activation-sparse Quantized Deep Neural Networks: Analysis and Mitigation using Sharpness-aware Training](https://mlsanity.com/paper/2406.10528/) (2024)
- [MDM: Manhattan Distance Mapping of DNN Weights for Parasitic-Resistance-Resilient Memristive Crossbars](https://mlsanity.com/paper/2511.04798/) (2025)

## Abstract (arXiv)

Recently several structured pruning techniques have been introduced for energy-efficient implementation of Deep Neural Networks (DNNs) with lesser number of crossbars. Although, these techniques have claimed to preserve the accuracy of the sparse DNNs on crossbars, none have studied the impact of the inexorable crossbar non-idealities on the actual performance of the pruned networks. To this end, we perform a comprehensive study to show how highly sparse DNNs, that result in significant crossbar-compression-rate, can lead to severe accuracy losses compared to unpruned DNNs mapped onto non-ideal crossbars. We perform experiments with multiple structured-pruning approaches (such as, C/F pruning, XCS and XRS) on VGG11 and VGG16 DNNs with benchmark datasets (CIFAR10 and CIFAR100). We propose two mitigation approaches - Crossbar column rearrangement and Weight-Constrained-Training (WCT) - that can be integrated with the crossbar-mapping of the sparse DNNs to minimize accuracy losses incurred by the pruned models. These help in mitigating non-idealities by increasing the proportion of low conductance synapses on crossbars, thereby improving their computational accuracies.