mlsanityData to 7 Oct 2026

Carbon-Efficient Neural Architecture Search

arXiv 2307.04131 ↗First posted 9 Jul 2023cs.LG cs.AIcited by 2

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Abstract (arXiv)

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 9 Jul 2023.

Builds on

25 of 32 references.

References · 25

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  3. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  4. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  5. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  6. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  7. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  8. 2017Progressive Neural Architecture Searchcited by 483
  9. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 412
  10. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 340
  11. 2019NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detectioncited by 289
  12. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
13 more
  1. 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
  2. 2016Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasetscited by 131
  3. 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvementcited by 115
  4. 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Searchcited by 87
  5. 2021HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmarkcited by 83
  6. 2020Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarkscited by 67
  7. 2019NAS-FCOS: Fast Neural Architecture Search for Object Detectioncited by 53
  8. 2020Few-shot Neural Architecture Searchcited by 50
  9. 2019Progressive DARTS: Bridging the Optimization Gap for NAS in the Wildcited by 39
  10. 2019AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Searchcited by 33
  11. 2021KNAS: Green Neural Architecture Searchcited by 33
  12. 2019Sample-Efficient Neural Architecture Search by Learning Action Spacecited by 23
  13. 2021Enabling Sustainable Clouds: The Case for Virtualizing the Energy Systemcited by 12

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  2. 2024CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Frameworkcited by 5