Characterizing the Energy Requirement of Computer Vision
The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This thesis aims to establish a baseline on how adjusting basic parameters can affect energy consum...
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| Formáid: | Tráchtas |
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Massachusetts Institute of Technology
2023
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| Rochtain ar líne: | https://hdl.handle.net/1721.1/151673 |
| _version_ | 1826213170186813440 |
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| author | Edelman, Daniel |
| author2 | Gadepally, Vijay N. |
| author_facet | Gadepally, Vijay N. Edelman, Daniel |
| author_sort | Edelman, Daniel |
| collection | MIT |
| description | The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This thesis aims to establish a baseline on how adjusting basic parameters can affect energy consumption in neural network learning on Computer Vision tasks. I catalogued the effects of various adjust adjustment from simple batch size adjustment to more complicated hardware configuration (such as power capping). Findings include that adjusting from single precision model to a mixed precision model can result in energy reductions of nearly 40%. Additionally power capping the GPU can reduce energy cost by an additional 10%. |
| first_indexed | 2024-09-23T15:44:45Z |
| format | Thesis |
| id | mit-1721.1/151673 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T15:44:45Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1516732023-08-01T03:09:46Z Characterizing the Energy Requirement of Computer Vision Edelman, Daniel Gadepally, Vijay N. Leiserson, Charles E. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science The energy requirements of neural network learning are growing at a rapid rate. Increased energy demands have caused a global need to seek ways to improve energy efficiency of neural network learning. This thesis aims to establish a baseline on how adjusting basic parameters can affect energy consumption in neural network learning on Computer Vision tasks. I catalogued the effects of various adjust adjustment from simple batch size adjustment to more complicated hardware configuration (such as power capping). Findings include that adjusting from single precision model to a mixed precision model can result in energy reductions of nearly 40%. Additionally power capping the GPU can reduce energy cost by an additional 10%. M.Eng. 2023-07-31T19:58:01Z 2023-07-31T19:58:01Z 2023-06 2023-06-06T16:35:41.150Z Thesis https://hdl.handle.net/1721.1/151673 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Edelman, Daniel Characterizing the Energy Requirement of Computer Vision |
| title | Characterizing the Energy Requirement of
Computer Vision |
| title_full | Characterizing the Energy Requirement of
Computer Vision |
| title_fullStr | Characterizing the Energy Requirement of
Computer Vision |
| title_full_unstemmed | Characterizing the Energy Requirement of
Computer Vision |
| title_short | Characterizing the Energy Requirement of
Computer Vision |
| title_sort | characterizing the energy requirement of computer vision |
| url | https://hdl.handle.net/1721.1/151673 |
| work_keys_str_mv | AT edelmandaniel characterizingtheenergyrequirementofcomputervision |