EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models

By Omar Shaikh, Jon Saad-Falcon, Austin P. Wright, Nilaksh Das, Scott Freitas, Omar Isaac Asensio, Duen Horng Chau. 2021

29 citations recorded by the source corpus.

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 present EnergyVis, 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.

Subfields: Artificial Intelligence, Computer Science Applications, Electrical and Electronic Engineering, Efficiency & Compression, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Noah A. Smith
2. Roy Schwartz
3. Jesse Dodge
4. Yejin Choi
5. Alexandra Sasha Luccioni
6. Emma Strubell
7. Mohit Bansal
8. Anne-Laure Ligozat

In Computer Science Applications

1. Noah A. Smith
2. Rémi Tachet des Combes
3. Alexandra Sasha Luccioni
4. Dan Jurafsky

In Electrical and Electronic Engineering

1. Anne-Laure Ligozat
2. Noah A. Smith
3. Alexandra Sasha Luccioni
4. Dan Jurafsky

In Efficiency & Compression

1. Noah A. Smith
2. Roy Schwartz
3. Jesse Dodge
4. Dan Jurafsky

In Evaluation & Benchmarks

1. Noah A. Smith
2. Roy Schwartz
3. Jesse Dodge
4. Aurélie Névéol
5. Anne-Laure Ligozat
6. Emma Strubell
7. Alexandra Sasha Luccioni
8. Yejin Choi

Citations in the corpus

• Measuring the Carbon Intensity of AI in Cloud Instances (2022, methodology): “Perhaps most similar to our work, EnergyVis [41] is an interactive tool for visualizing and comparing energy consumption of ML models as a function of hardware and physical location (U.S. state), given metadata about a model’s energy use per epoch.”
• Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (2021, background): “Other tools under development were also excluded, when they provided no code or online platform: (Zhang et al., 2020; Shaikh et al., 2021).”