mlsanityData to 7 Oct 2026

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

arXiv 2107.06871 ↗First posted 6 Jul 2021cs.AR cs.LGcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 6 Jul 2021.

Builds on

16 of 28 references.

References · 16

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  4. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  5. 2017Efficient Processing of Deep Neural Networks: A Tutorial and Surveycited by 427
  6. 2019Hardware/Software Co-Exploration of Neural Architecturescited by 50
  7. 2020Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Taskscited by 43
  8. 2019Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Searchcited by 38
  9. 2020EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutionscited by 35
  10. 2019Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Acceleratorscited by 32
  11. 2020Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Startcited by 22
  12. 2019On Neural Architecture Search for Resource-Constrained Hardware Platformscited by 18
4 more
  1. 2020MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentationcited by 16
  2. 2019When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remediescited by 14
  3. 2020NASS: Optimizing Secure Inference via Neural Architecture Searchcited by 7
  4. 2020Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 6; 2024: 3; 2025: 2; 2026: 3.

17 papers.

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  1. 2022SWIM: Selective Write-Verify for Computing-in-Memory Neural Acceleratorscited by 16
  2. 2022Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?cited by 12
  3. 2024Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architecturescited by 12
  4. 2023On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Acceleratorscited by 11
  5. 2023AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computingcited by 10
  6. 2021RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Searchcited by 8
  7. 2023Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noisecited by 8
  8. 2024TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Acceleratorscited by 3
  9. 2023NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Acceleratorscited by 2
  10. 2023Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protectionscited by 2
  11. 2024Multi-Objective Neural Architecture Search for In-Memory Computingcited by 1
  12. 2025CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Searchcited by 1
5 more
  1. 2023U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Acceleratorscited by 0
  2. 2025CrossNAS: A Cross-Layer Neural Architecture Search Framework for PIM Systemscited by 0
  3. 2026CiMRAG: CiM-Aware Domain-Adaptive and Noise-Resilient Retrieval-Augmented Generation for Edge-Based LLMscited by 0
  4. 2026HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Searchcited by 0
  5. 2026LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networkscited by 0