What does this research mean for the field?

The CAS-NAS framework achieves 30%–42% reductions in energy consumption and 28%–38% lower carbon emissions compared to ResNet-50 and EfficientNet baselines while maintaining competitive accuracy. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

The aim is to create a framework that minimizes energy consumption and carbon emissions in AI models while maintaining accuracy.

February 26, 2026Engineering Science and Technology an International JournalOpen Access

CAS-NAS: A carbon-aware neural architecture search framework for sustainable AI development

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Authors

STShahriar Ahsan Taisiq

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Overview

This research develops a framework to optimize energy efficiency and carbon footprint in AI, suggesting significant reductions in emissions and energy use.

Key Points

The aim is to create a framework that minimizes energy consumption and carbon emissions in AI models while maintaining accuracy.
Developed a carbon-aware neural architecture search framework using NSGA-II-based optimization.
Utilized real-time carbon intensity simulation and hardware-aware energy estimation.
Evaluated models using proxy datasets (CIFAR-10/100) and various hardware profiles.
Achieved 30%–42% reductions in energy consumption and 28%–38% reduction in carbon emissions compared to baseline models.
Demonstrated a trade-off where a 1% accuracy reduction led to 45%–60% energy savings.
Established standardized metrics for Green AI, enhancing sustainability comparisons.

Cite This Study

Shahriar Ahsan Taisiq (2026) studied this question.

synapsesocial.com/papers/699fe2eb95ddcd3a253e65e1 https://doi.org/10.1016/j.jestch.2026.102313

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February 26, 2026Engineering Science and Technology an International JournalOpen Access

CAS-NAS: A carbon-aware neural architecture search framework for sustainable AI development

View Full Paper

Ask AI

Bookmark

Authors

STShahriar Ahsan Taisiq

Discussion

Member takes

Overview

This research develops a framework to optimize energy efficiency and carbon footprint in AI, suggesting significant reductions in emissions and energy use.

Key Points

The aim is to create a framework that minimizes energy consumption and carbon emissions in AI models while maintaining accuracy.
Developed a carbon-aware neural architecture search framework using NSGA-II-based optimization.
Utilized real-time carbon intensity simulation and hardware-aware energy estimation.
Evaluated models using proxy datasets (CIFAR-10/100) and various hardware profiles.
Achieved 30%–42% reductions in energy consumption and 28%–38% reduction in carbon emissions compared to baseline models.
Demonstrated a trade-off where a 1% accuracy reduction led to 45%–60% energy savings.
Established standardized metrics for Green AI, enhancing sustainability comparisons.

Cite This Study

Shahriar Ahsan Taisiq (2026) studied this question.

synapsesocial.com/papers/699fe2eb95ddcd3a253e65e1 https://doi.org/10.1016/j.jestch.2026.102313

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