Omar Shaikh

EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models

Jon Saad-Falcon
Austin P Wright
Scott Freitas
Omar Isaac Asensio
CHI '21 Extended Abstracts (CHI LBW), 2021

Abstract

The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We presentEnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage.

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BibTeX

			
@inproceedings{shaikhEnergyVis2021,
author = {Shaikh, Omar and Saad-Falcon, Jon and Wright, Austin P and Das, Nilaksh and Freitas, Scott and Asensio, Omar and Chau, Duen Horng},
title = {EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models},
year = {2021},
isbn = {9781450380959},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3411763.3451780},
doi = {10.1145/3411763.3451780},
booktitle = {Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems},
articleno = {458},
numpages = {7},
keywords = {environmental sustainability, computational equity, machine learning, interactive visualization},
location = {Yokohama, Japan},
series = {CHI EA '21}
}