# Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise

arXiv: https://arxiv.org/abs/2109.05437 · HTML: https://mlsanity.com/paper/2109.05437/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients](https://mlsanity.com/paper/1606.06160/) (2016)
- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization](https://mlsanity.com/paper/2102.10462/) (2021)
- [Trading-off Accuracy and Energy of Deep Inference on Embedded Systems: A Co-Design Approach](https://mlsanity.com/paper/1901.10584/) (2019)
- [RED: A ReRAM-based Deconvolution Accelerator](https://mlsanity.com/paper/1907.02987/) (2019)

## Built on by (most cited first, 12 of 12)

- [Approximate Computing and the Efficient Machine Learning Expedition](https://mlsanity.com/paper/2210.00497/) (2022)
- [Preference-Aware Constrained Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2303.13034/) (2023)
- [Towards Efficient IMC Accelerator Design Through Joint Hardware-Workload Co-optimization](https://mlsanity.com/paper/2410.16759/) (2024)
- [Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures](https://mlsanity.com/paper/2501.09588/) (2025)
- [Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions](https://mlsanity.com/paper/2212.13707/) (2022)
- [TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference](https://mlsanity.com/paper/2303.14882/) (2023)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [End-to-End Transformer Acceleration Through Processing-in-Memory Architectures](https://mlsanity.com/paper/2601.14260/) (2025)
- [QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design](https://mlsanity.com/paper/2601.14549/) (2026)
- [Non-Myopic Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2412.08085/) (2024)
- [Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators](https://mlsanity.com/paper/2603.03880/) (2026)
- [ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures](https://mlsanity.com/paper/2607.17091/) (2026)

## Abstract (arXiv)

Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM crossbars can degrade the DNN inferencing accuracy. We propose the design and optimization of a high-performance, area-and energy-efficient ReRAM-based hardware accelerator to achieve robust DNN inferencing in the presence of stochastic noise. We make two key technical contributions. First, we propose a stochastic-noise-aware training method, referred to as ReSNA, to improve the accuracy of DNN inferencing on ReRAM crossbars with stochastic noise. Second, we propose an information-theoretic algorithm, referred to as CF-MESMO, to identify the Pareto set of solutions to trade-off multiple objectives, including inferencing accuracy, area overhead, execution time, and energy consumption. The main challenge in this context is that executing the ReSNA method to evaluate each candidate ReRAM design is prohibitive. To address this challenge, we utilize the continuous-fidelity evaluation of ReRAM designs associated with prohibitive high computation cost by varying the number of training epochs to trade-off accuracy and cost. CF-MESMO iteratively selects the candidate ReRAM design and fidelity pair that maximizes the information gained per unit computation cost about the optimal Pareto front. Our experiments on benchmark DNNs show that the proposed algorithms efficiently uncover high-quality Pareto fronts. On average, ReSNA achieves 2.57% inferencing accuracy improvement for ResNet20 on the CIFAR-10 dataset with respect to the baseline configuration. Moreover, CF-MESMO algorithm achieves 90.91% reduction in computation cost compared to the popular multi-objective optimization algorithm NSGA-II to reach the best solution from NSGA-II.