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 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%.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Edelman, Daniel

Advisors dc:contributor.advisor

• Gadepally, Vijay N.
• Leiserson, Charles E.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151673

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151673