# EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search

arXiv: https://arxiv.org/abs/2210.06015 · HTML: https://mlsanity.com/paper/2210.06015/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 52 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search](https://mlsanity.com/paper/2001.00326/) (2020)
- [NAS-Bench-101: Towards Reproducible Neural Architecture Search](https://mlsanity.com/paper/1902.09635/) (2019)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022)
- [Neural Predictor for Neural Architecture Search](https://mlsanity.com/paper/1912.00848/) (2019)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark](https://mlsanity.com/paper/2103.10584/) (2021)
- [Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks](https://mlsanity.com/paper/2008.09777/) (2020)
- [NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search](https://mlsanity.com/paper/2001.10422/) (2020)
- [Tabular Benchmarks for Joint Architecture and Hyperparameter Optimization](https://mlsanity.com/paper/1905.04970/) (2019)
- [Few-shot Neural Architecture Search](https://mlsanity.com/paper/2006.06863/) (2020)
- [KNAS: Green Neural Architecture Search](https://mlsanity.com/paper/2111.13293/) (2021)
- [Constructing Fast Network through Deconstruction of Convolution](https://mlsanity.com/paper/1806.07370/) (2018)
- [NAS-Bench-x11 and the Power of Learning Curves](https://mlsanity.com/paper/2111.03602/) (2021)
- [Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization](https://mlsanity.com/paper/2105.01015/) (2021)
- [Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis](https://mlsanity.com/paper/2203.02202/) (2022)
- [Neural Architecture Search for Speech Emotion Recognition](https://mlsanity.com/paper/2203.16928/) (2022)

## Built on by (most cited first, 11 of 11)

- [Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI](https://mlsanity.com/paper/2309.02065/) (2023)
- [Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations](https://mlsanity.com/paper/2403.08151/) (2024)
- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/2403.12562/) (2024)
- [Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race](https://mlsanity.com/paper/2502.20016/) (2025)
- [Kernel-Level Energy-Efficient Neural Architecture Search for Tabular Dataset](https://mlsanity.com/paper/2504.08359/) (2025)
- [One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection](https://mlsanity.com/paper/2505.01468/) (2025)
- [Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks](https://mlsanity.com/paper/2505.15631/) (2025)
- [EBA-AI: Ethics-Guided Bias-Aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring](https://mlsanity.com/paper/2507.15036/) (2025)
- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting](https://mlsanity.com/paper/2509.24517/) (2025)

## Abstract (arXiv)

Energy consumption from the selection, training, and deployment of deep learning models has seen a significant uptick recently. This work aims to facilitate the design of energy-efficient deep learning models that require less computational resources and prioritize environmental sustainability by focusing on the energy consumption. Neural architecture search (NAS) benefits from tabular benchmarks, which evaluate NAS strategies cost-effectively through precomputed performance statistics. We advocate for including energy efficiency as an additional performance criterion in NAS. To this end, we introduce an enhanced tabular benchmark encompassing data on energy consumption for varied architectures. The benchmark, designated as EC-NAS, has been made available in an open-source format to advance research in energy-conscious NAS. EC-NAS incorporates a surrogate model to predict energy consumption, aiding in diminishing the energy expenditure of the dataset creation. Our findings emphasize the potential of EC-NAS by leveraging multi-objective optimization algorithms, revealing a balance between energy usage and accuracy. This suggests the feasibility of identifying energy-lean architectures with little or no compromise in performance.