# RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

arXiv: https://arxiv.org/abs/1809.00072 · HTML: https://mlsanity.com/paper/1809.00072/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 19 of 19)

- [Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators](https://mlsanity.com/paper/2302.08469/) (2023)
- [A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays](https://mlsanity.com/paper/2104.02184/) (2021)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks](https://mlsanity.com/paper/2210.12899/) (2022)
- [SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks](https://mlsanity.com/paper/2108.08903/) (2021)
- [Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks](https://mlsanity.com/paper/2201.05229/) (2022)
- [Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing](https://mlsanity.com/paper/2305.18416/) (2023)
- [ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation](https://mlsanity.com/paper/2402.02586/) (2024)
- [Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks](https://mlsanity.com/paper/2105.04003/) (2021)
- [AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks](https://mlsanity.com/paper/2210.10048/) (2022)
- [TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing](https://mlsanity.com/paper/2408.12742/) (2024)
- [XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars](https://mlsanity.com/paper/2302.07769/) (2023)
- [Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures](https://mlsanity.com/paper/2308.06227/) (2023)
- [LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators](https://mlsanity.com/paper/2312.03146/) (2023)
- [When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design](https://mlsanity.com/paper/2408.12767/) (2024)
- [Running Conventional Automatic Speech Recognition on Memristor Hardware: A Simulated Approach](https://mlsanity.com/paper/2505.24721/) (2025)
- [SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks](https://mlsanity.com/paper/2101.07864/) (2021)
- [ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing](https://mlsanity.com/paper/2605.20802/) (2026)
- [Positional Encoding in the Context of Memristor-Based Analog Computation for Automatic Speech Recognition](https://mlsanity.com/paper/2606.13379/) (2026)

## Abstract (arXiv)

Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly and efficiently realize vector-matrix multiplication (VMM), the dominant computational kernel in DNNs. However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level non-idealities such as interconnect parasitics, peripheral circuits, sneak paths, and process variations. These non-idealities can lead to errors in VMMs, eventually degrading the DNN's accuracy. It is therefore critical to study the impact of crossbar non-idealities on the accuracy of large-scale DNNs. However, this is challenging because existing device and circuit models are too slow to use in application-level evaluations. We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems. RxNN splits and maps the computations involved in each DNN layer into crossbar operations, and evaluates them using a Fast Crossbar Model (FCM) that accurately captures the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation. FCM models a crossbar-based VMM operation using three stages - non-linear models for the input and output peripheral circuits (DACs and ADCs), and an equivalent non-ideal conductance matrix for the core crossbar array. We implement RxNN by extending the Caffe machine learning framework and use it to evaluate a suite of six large-scale DNNs developed for the ImageNet Challenge. Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6%-32%) for these large-scale DNNs. To the best of our knowledge, this work is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware.