ECO2AI: Machine Learning Model Carbon Emission Tracking Tool for Sustainable AI

Table of Contents

• 1 Gabatarwa
• 2 Hanyar
  • 2.1 Binciken Amfani da Makamashi
  • 2.2 Lissafin Hayakin Carbon na Yanki
• 3 Technical Implementation
  • 3.1 Mathematical Formulas
  • 3.2 Code Examples
• 4 Experimental Results
  • 4.1 Energy Consumption Analysis
  • 4.2 Carbon Emission Comparison
• 5 Original Analysis
• 6 Aikace-aikace na Gaba
• 7 Manazarta

1 Gabatarwa

The exponential growth in the scale and complexity of deep neural networks has significantly increased energy consumption during training and inference processes. ECO2AI addresses this issue by providing an open-source toolkit to track the energy consumption and equivalent carbon emissions of machine learning models. This tool emphasizes precise energy tracking and regional carbon emission accounting, encouraging the research community to develop AI architectures with lower computational costs.

2 Hanyar

2.1 Binciken Amfani da Makamashi

ECO2AI monitors hardware-level power consumption through system-specific APIs and sensors, tracking CPU, GPU, and memory usage during model training and inference phases.

2.2 Lissafin Hayakin Carbon na Yanki

This tool integrates regional carbon intensity data and calculates equivalent carbon emissions based on energy consumption patterns and local grid characteristics.

3 Technical Implementation

3.1 Mathematical Formulas

Carbon emission calculation formula is: $CO_2 = E \times CI$, where $E$ is energy consumption (unit: kilowatt-hour), $CI$ is carbon intensity factor (unit: kilogram carbon dioxide/kilowatt-hour). Energy consumption calculation formula is: $E = P \times t$, where $P$ is power (unit: kilowatt), $t$ is time (unit: hour).

3.2 Code Examples

import eco2ai

4 Experimental Results

4.1 Energy Consumption Analysis

Gwajin ya nuna, horar da daidaitaccen ResNet-50 model yakan cinye kimanin wutar lantarki kilowatt-hours 45, kuma a matsakaicin yankin carbon intensity yana daidai da samar da carbon dioxide kilogiram 22.

4.2 Carbon Emission Comparison

Wannan binciken ya kwatanta adadin hayakin carbon a yankuna daban-daban, ya bayyana bambance-bambance masu mahimmanci dangane da hanyoyin samar da makamashi na gida.

5 Original Analysis

Tsarin ECO2AI yana wakiltar babban ci gaba a cikin ci gaban AI mai dorewa, yana amsa buƙatar buɗe tasirin muhalli na injinan koyo. Kamar yadda CycleGAN (Zhu et al., 2017) ta kawo juyin juya hali a fannin fassarar hoto mara kulawa, ECO2AI ta fara kafa daidaitaccen tsarin lissafin carbon don ayyukan AI. Hanyar lissafin hayaƙin yanki na kayan aikin ta kasance mai ƙirƙira musamman, tana gane bambance-bambance masu yawa a cikin ƙarfin carbon na wurare daban-daban - wani abu da aka yi watsi da shi a cikin ma'auni na dorewa a baya.

Compared to existing solutions like CodeCarbon and Carbontracker, ECO2AI demonstrates higher accuracy in hardware-level power consumption monitoring and integrates more comprehensive regional data. According to the International Energy Agency's 2022 report, data centers currently consume approximately 1% of global electricity, with AI workloads becoming a rapidly growing segment. This methodology aligns with the ESG framework, which has gained significant attention post-Paris Agreement, providing quantifiable metrics for corporate sustainability reporting.

The technical implementation demonstrates sophistication through a multi-layer monitoring scheme, tracking not only GPU usage but also encompassing CPU, memory, and storage energy consumption. Such comprehensive monitoring is crucial, as research from Lawrence Berkeley National Laboratory indicates that in machine learning workflows, auxiliary components can contribute up to 30% of the system's total energy consumption. The mathematical formula, while conceptually concise, effectively captures the essential relationship between computational effort and environmental impact.

This study simultaneously advances both Sustainable AI (optimizing existing model efficiency) and Green AI (developing new efficient architectures), forming a feedback loop that can significantly reduce the carbon footprint of AI development. As the AI industry continues to grow exponentially, tools like ECO2AI will become increasingly important for ensuring that technological progress aligns with environmental sustainability goals.

6 Aikace-aikace na Gaba

Hanyoyin ci gaba na gaba sun haɗa da haɗawa da dandamali na girgije, sa ido kan hayaki na lokaci-lokaci, da shawarwarin ingantawa ta atomatik don rage carbon footprint. Kayan aikin za a iya faɗaɗa su zuwa rufe cikakken tsarin rayuwar ML daga shirye-shiryen bayanai zuwa turawa samfuri.

7 Manazarta

1. Budennyy, S. et al. ECO2AI: Carbon emissions tracking of machine learning models. arXiv:2208.00406 (2022)
2. Zhu, J. Y. et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. ICCV (2017)
3. International Energy Agency. Data Centres and Data Transmission Networks (2022)
4. Schwartz, R. et al. Green AI. Communications of the ACM (2020)
5. Strubell, E. et al. Energy and Policy Considerations for Deep Learning in Natural Language Processing. ACL (2019)