Full metadata
Title
Analyzing the Carbon Emissions Impact of AI-Enabled Chips
Description
This paper delves into the carbon footprint generated by AI chips during their training and operational phases. It highlights the often-overlooked environmental impact of training AI models like ChatGPT, emphasizing the significant CO2 emissions and computational demands involved. The paper also explores the paradoxical nature of AI, which, while contributing to climate change, also holds potential in combating its effects. This dual role of AI sparks ethical debates, particularly concerning strategies to minimize the carbon emissions associated with AI training. Some potential solutions, such as increased transparency among AI-utilizing companies and the adoption of analog-in-memory computing, to address these challenges while also continuing to push the boundaries of AI computing.
Date Created
2023-12
Contributors
- Mulvey, Nicole (Author)
- Marinella, Matthew (Thesis director)
- Short, Jesse (Committee member)
- Barrett, The Honors College (Contributor)
- Electrical Engineering Program (Contributor)
Topical Subject
Resource Type
Extent
22 pages
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2023-2024
Handle
https://hdl.handle.net/2286/R.2.N.190272
System Created
- 2023-11-17 08:28:37
System Modified
- 2023-11-29 05:30:10
- 1 year ago
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