# Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

arXiv: https://arxiv.org/abs/2107.06871 · HTML: https://mlsanity.com/paper/2107.06871/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Hardware/Software Co-Exploration of Neural Architectures](https://mlsanity.com/paper/1907.04650/) (2019)
- [Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks](https://mlsanity.com/paper/2002.04116/) (2020)
- [Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search](https://mlsanity.com/paper/1901.11211/) (2019)
- [EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions](https://mlsanity.com/paper/2005.02563/) (2020)
- [Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/1911.00139/) (2019)
- [Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start](https://mlsanity.com/paper/2007.09087/) (2020)
- [On Neural Architecture Search for Resource-Constrained Hardware Platforms](https://mlsanity.com/paper/1911.00105/) (2019)
- [MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation](https://mlsanity.com/paper/2007.06151/) (2020)
- [When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies](https://mlsanity.com/paper/1909.04697/) (2019)
- [NASS: Optimizing Secure Inference via Neural Architecture Search](https://mlsanity.com/paper/2001.11854/) (2020)
- [Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation](https://mlsanity.com/paper/2008.07071/) (2020)

## Built on by (most cited first, 17 of 17)

- [SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2202.08395/) (2022)
- [Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?](https://mlsanity.com/paper/2207.07626/) (2022)
- [Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures](https://mlsanity.com/paper/2405.04700/) (2024)
- [On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators](https://mlsanity.com/paper/2306.06923/) (2023)
- [AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing](https://mlsanity.com/paper/2305.10459/) (2023)
- [RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search](https://mlsanity.com/paper/2109.05691/) (2021)
- [Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise](https://mlsanity.com/paper/2307.15853/) (2023)
- [Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections](https://mlsanity.com/paper/2312.06137/) (2023)
- [TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators](https://mlsanity.com/paper/2406.06544/) (2024)
- [NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators](https://mlsanity.com/paper/2305.14561/) (2023)
- [Multi-Objective Neural Architecture Search for In-Memory Computing](https://mlsanity.com/paper/2406.06746/) (2024)
- [CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search](https://mlsanity.com/paper/2509.25862/) (2025)
- [U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators](https://mlsanity.com/paper/2401.05357/) (2023)
- [CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systems](https://mlsanity.com/paper/2505.22868/) (2025)
- [CiMRAG: CiM-Aware Domain-Adaptive and Noise-Resilient Retrieval-Augmented Generation for Edge-Based LLMs](https://mlsanity.com/paper/2601.20041/) (2026)
- [HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search](https://mlsanity.com/paper/2605.04103/) (2026)
- [LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks](https://mlsanity.com/paper/2606.10294/) (2026)

## Abstract (arXiv)

Emerging device-based Computing-in-memory (CiM) has been proved to be a promising candidate for high-energy efficiency deep neural network (DNN) computations. However, most emerging devices suffer uncertainty issues, resulting in a difference between actual data stored and the weight value it is designed to be. This leads to an accuracy drop from trained models to actually deployed platforms. In this work, we offer a thorough analysis of the effect of such uncertainties-induced changes in DNN models. To reduce the impact of device uncertainties, we propose UAE, an uncertainty-aware Neural Architecture Search scheme to identify a DNN model that is both accurate and robust against device uncertainties.