AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models

Technical Deep Dive: AI-CARE

Overview

AI-CARE is a standardized framework for carbon-aware evaluation of machine learning models. It reports predictive performance alongside energy consumption and derived carbon emissions, enabling transparent and reproducible analysis of deployment-relevant efficiency trade-offs.

Problem & Context

Existing ML benchmarks focus narrowly on predictive performance metrics like accuracy, BLEU, or mAP, while largely ignoring the environmental cost of model training and inference. This single-objective evaluation paradigm is increasingly misaligned with real-world deployment requirements, particularly in energy-constrained environments.

Without explicit reporting of energy and carbon impacts, practitioners lack the information needed to make informed, sustainability-aware model selection decisions. Current tooling primarily reports raw measurements without prescribing how such data should be normalized, compared, or interpreted alongside task performance.

Methodology

AI-CARE is designed as a reporting and analysis framework that integrates accuracy, energy, and carbon metrics in a coherent and interpretable way. Key features:

• Reporting-centric: Focuses on standardized reporting rather than model optimization or control
• Low overhead: Performs energy monitoring and carbon-emission retrieval asynchronously to avoid interfering with model execution
• Interpretable: Provides normalization, aggregation, and visualization mechanisms (e.g., carbon–accuracy tradeoff curves, scalar carbon-aware scores)

The framework models energy consumption using FLOPs, memory accesses, and static power, supporting both analytical estimation and empirical measurement. Carbon emissions are derived from energy usage and a fixed grid intensity.

Data & Experimental Setup

Experiments were conducted on five computer vision benchmarks: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and ImageNet-100. Representative architectures spanning MLPs, CNNs, transformers, and MLP-Mixers were evaluated under consistent training settings.

Inference-time carbon emissions were computed using a fixed grid carbon intensity of 400 gCO2/kWh, consistent with recent global average estimates.

Results

The carbon–performance tradeoff plots reveal consistent, nonlinear efficiency disparities:

• Incremental gains in predictive performance often require disproportionately higher carbon expenditure, particularly as dataset complexity increases.
• Deeper or more computationally intensive architectures cluster in high-emission regions, offering only marginal performance improvements over lightweight alternatives.
• Models with comparable accuracy can exhibit non-trivial differences in precision, recall, or F1, while differing by orders of magnitude in total carbon emissions.

The scalar carbon-aware scores complement this geometric analysis, exposing metric-dependent variations in model rankings and highlighting architectures that achieve favorable performance–emissions balance across datasets.

Interpretation

The combined results demonstrate that carbon-aware evaluation fundamentally reshapes model assessment compared to conventional performance-only benchmarking. Sustainability should be treated as a first-class evaluation dimension, as energy and carbon impact materially influence empirical conclusions about model quality.

By jointly reporting predictive performance, energy consumption, and carbon emissions, AI-CARE reveals efficiency trade-offs that remain obscured under accuracy-centric evaluation. The framework provides structured, deployment-relevant evidence that carbon-aware reporting is necessary to support informed, sustainability-aware model selection.

Limitations & Uncertainties

• The study uses a fixed grid carbon intensity value, which may not reflect real-time or location-specific variations.
• Only a limited set of model families and vision benchmarks were evaluated; results may differ for other domains or architectures.
• The framework does not yet integrate hardware-specific energy measurements or context-dependent carbon factors.

What Comes Next

Future work aims to:

• Enhance the framework with hardware-specific energy monitoring and dynamic grid carbon intensity models
• Expand the evaluation to broader domains beyond computer vision
• Investigate the integration of carbon-aware reporting into existing ML benchmarking and optimization workflows

Sources:

• AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models