mlsanityData to 7 Oct 2026

FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models

arXiv 2402.06696 ↗First posted 9 Feb 2024cs.LG cs.AIcited by 9

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Abstract (arXiv)

Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by mobile and edge devices with limited computing resources. The emerging large language models (LLMs), due to their prowess, have also been incorporated into NAS recently and show some promising results. This paper conducts further exploration in this direction by considering three important design metrics simultaneously, i.e., model accuracy, fairness, and hardware deployment efficiency. We propose a novel LLM-based NAS framework, FL-NAS, in this paper, and show experimentally that FL-NAS can indeed find high-performing DNNs, beating state-of-the-art DNN models by orders-of-magnitude across almost all design considerations.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 24 of its 31 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

24 of 31 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  6. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  7. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  8. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
  9. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  10. 2023PaLM 2 Technical Reportcited by 1,154
  11. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  12. 2021A White Paper on Neural Network Quantizationcited by 453
12 more
  1. 2020A Survey on Evolutionary Neural Architecture Searchcited by 142
  2. 2019FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Searchcited by 129
  3. 2019Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hourscited by 87
  4. 2023Can GPT-4 Perform Neural Architecture Search?cited by 74
  5. 2020EcoNAS: Finding Proxies for Economical Neural Architecture Searchcited by 61
  6. 2020EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutionscited by 35
  7. 2022The Larger The Fairer? Small Neural Networks Can Achieve Fairness for Edge Devicescited by 21
  8. 2022SWIM: Selective Write-Verify for Computing-in-Memory Neural Acceleratorscited by 16
  9. 2023On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Acceleratorscited by 11
  10. 2021FNAS: Uncertainty-Aware Fast Neural Architecture Searchcited by 6
  11. 2023DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architecturescited by 6
  12. 2022On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 3.

9 papers.

  1. 2024Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devicescited by 17
  2. 2024Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architecturescited by 12
  3. 2025SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Modelscited by 7
  4. 2026Bilevel Optimization for Neural Architecture Searchcited by 2
  5. 2024NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMscited by 1
  6. 2024Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edgecited by 1
  7. 2025ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architecturescited by 1
  8. 2026DeepFedNAS: Efficient Hardware-Aware Architecture Adaptation for Heterogeneous IoT Federations via Pareto-Guided Supernet Trainingcited by 1
  9. 2026FairCompressAgent: An Agentic Framework for Fairness-Aware Model Compression for FPGA Deploymentcited by 0