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

@article{Jain2021RxNNAF, title={RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars}, author={Shubham Jain and Abhronil Sengupta and Kaushik Roy and Anand Raghunathan}, journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, year={2021}, volume={40}, pages={326-338} }

Resistive crossbars have emerged as promising building blocks for realizing DNNs due to their ability to compactly and efficiently realize the dominant DNN computational kernel, viz., vector-matrix multiplication. [] Key Method Finally, we develop RxNN, a software framework to evaluate and re-train DNNs on resistive crossbar systems. RxNN is based on the popular Caffe machine learning framework, and we use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge (ILSVRC). Our…

## 41 Citations

### TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems

- Computer ScienceIEEE Transactions on Very Large Scale Integration (VLSI) Systems
- 2021

TxSim is proposed, a fast and customizable modeling framework to functionally evaluate DNN training on crossbar-based hardware considering the impact of nonidealities and achieves computational efficiency by mapping crossbar evaluations to well-optimized Basic Linear Algebra Subprograms routines and incorporates speedup techniques to further reduce simulation time with minimal impact on accuracy.

### X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

- Computer ScienceArXiv
- 2019

This work proposes crossbar re-mapping strategies to mitigate line-resistance induced accuracy degradation in DNNs, without having to re-train the learned weights, unlike most prior works.

### Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars

- Computer ScienceISLPED
- 2022

This paper conducts a comprehensive analysis of the robustness of SNNs on non-ideal crossbars and shows that repetitive crossbar computations across multiple time-steps induce error accumulation, resulting in a huge performance drop during SNN inference.

### Modeling and Mitigating the Interconnect Resistance Issue in Analog RRAM Matrix Computing Circuits

- Computer ScienceIEEE Transactions on Circuits and Systems I: Regular Papers
- 2022

This work develops a physics-based iterative algorithm to quickly model the matrix-vector multiplication (MVM) operation of crosspoint resistive array with interconnect resistances, thus quadratically reducing the time complexity of circuit simulation.

### Modeling and simulating in-memory memristive deep learning systems: An overview of current efforts

- Computer ScienceArray
- 2022

### Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

- Computer ScienceSemiconductor Science and Technology
- 2021

This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system’s sensitivity to parasitic resistance effects.

### Magnetoresistive Circuits and Systems: Embedded Non-Volatile Memory to Crossbar Arrays

- Computer ScienceIEEE Transactions on Circuits and Systems I: Regular Papers
- 2021

Various tradeoffs and design challenges of MRAM are discussed in three broad application areas: 1) embedded non-volatile memory (eNVMs), 2) crossbar-based analog in-memory computing, and 3) stochastic computing.

### Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges

- Computer ScienceProceedings of the IEEE
- 2020

This work describes the design principles of resistive crossbars, including the devices and associated circuits that constitute them, and discusses intrinsic approximations arising from the device and circuit characteristics and study their functional impact on the MVM operation.

### NEAT: Non-linearity Aware Training for Accurate and Energy-Efficient Implementation of Neural Networks on 1T-1R Memristive Crossbars

- Computer ScienceArXiv
- 2020

A novel Non-linearity Aware Training (NEAT) method to address the non-idealities of the 1T-1R crossbar and finds that each layer has a different weight distribution and in turn requires different gate voltage of transistor to guarantee linear operation.

### SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks

- Computer ScienceArXiv
- 2021

This work proposes a methodology, SEMULATOR (SiMULATOR by Emulating the analog computing block) which uses a deep neural network to emulate the behavior of crossbar-based analog computing system and experimentally and theoretically shows that it emulates a MAC unit for neural computation.

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