Green AI Quotient: Assessing Greenness of AI-based software and the way forward-Reference-Cited by-同舟云学术

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Green AI Quotient: Assessing Greenness of AI-based software and the way forward

Published:2023-09-11 Issue: Volume: Page:
ISSN:
Container-title:2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
language:
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Author:

Sikand Samarth¹,Sharma Vibhu Saujanya¹,Kaulgud Vikrant¹,Podder Sanjay²

Affiliation:

1. Accenture Labs,India

2. Accenture,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10298258/10298286/10298412.pdf?arnumber=10298412

Reference29 articles.

1. Efficient Processing of Deep Neural Networks: A Tutorial and Survey

2. Green AI

3. Chasing Carbon: The Elusive Environmental Footprint of Computing

4. Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

5. DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Focus on Carbon Dioxide Footprint of AI/ML Model Training;2024 Intelligent Methods, Systems, and Applications (IMSA);2024-07-13

2. Exploring Perspectives, Issues, and Practices in the Testing and Quality Validation of AI Software;2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE);2024-05-14

3. Eco-Friendly AI: A Guide to Energy-Efficient Techniques Across the AI Life-Cycle;Communications in Computer and Information Science;2024

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