Learn to Code Sustainably: An Empirical Study on Green Code Generation-Reference-Cited by-同舟云学术

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Learn to Code Sustainably: An Empirical Study on Green Code Generation

Published:2024-04-20 Issue: Volume:31 Page:30-37
ISSN:
Container-title:Proceedings of the 1st International Workshop on Large Language Models for Code
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Author:

Vartziotis Tina¹²^ORCID,Dellatolas Ippolyti³^ORCID,Dasoulas George⁴^ORCID,Schmidt Maximilian¹^ORCID,Schneider Florian¹^ORCID,Hoffmann Tim⁵^ORCID,Kotsopoulos Sotirios⁶^ORCID,Keckeisen Michael¹^ORCID

Affiliation:

1. TWT Science and Innovation, Stuttgart, Germany

2. NTUA, Athens, Greece

3. MIT, Cambridge, Massachusetts, USA

4. Harvard University, Cambridge, Massachusetts, USA

5. Mercedes-Benz, Stuttgart, Germany

6. National Technical University of Athens, Massachusetts Institute of Technology, Stuttgart, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3643795.3648394

Reference33 articles.

1. Carbon Dependencies in Datacenter Design and Management

2. Amodei and Hernandez. 2018. . https://openai.com/research/ai-and-compute

3. Mahmoud Assran Joshua Romoff Nicolas Ballas Joelle Pineau and Michael Rabbat. 2019. Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning. (2019). 10.48550/ARXIV.1906.04585

4. Alfredo Canziani Adam Paszke and Eugenio Culurciello. 2016. An Analysis of Deep Neural Network Models for Practical Applications. 10.48550/ARXIV.1605.07678

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