## about us

Our goal is to measure, understand, optimize, and expose the energy consumption of modern machine learning.

The ML.ENERGY Initiative is a joint effort of computer scientists across multiple academic institutions, including SymbioticLab where it originally started.

#### Understand

Observing and understanding energy consumption of modern ML

#### Optimize

Optimizing ML energy consumption based on understandings

#### Expose

Exposing energy to ML developers to encourage optimization

## Projects

How much energy do modern large language models (LLMs) consume?

Website

Deep learning energy measurement and optimization

Website

## Members

### Faculty

#### Mosharaf Chowdhury

University of Michigan

#### Adam Belay

MIT CSAIL

#### Asaf Cidon

Columbia University

#### Beidi Chen

CMU

#### Simon Peter

University of Washington

#### Tom Anderson

University of Washington

### Students

#### Jae-Won Chung

###### Ph.D. Student

University of Michigan

#### Jiachen Liu

###### Ph.D. Student

University of Michigan

### Alumni

#### Jie You

###### Alumnus, Ph.D.

#### Luoxi Meng

###### Alumnus, MS

#### Yile Gu

###### Alumnus, MS

#### Zhenning Yang

###### Alumnus, MS

## News

- August 7th, 2024

#### Perseus → SOSP '24

Perseus, an energy optimization system for large model training, was accepted to appear at SOSP '24!

Paper Blog

- May 11th, 2024

#### PyTorch Ecosystem project

Zeus is now part of the PyTorch Ecosystem!

PyTorch blog

- Feb 29th, 2024

#### Mozilla Tech Fund 2024

Zeus receives $50,000 for development support from the 2024 Mozilla Technology Fund.

Announcement

- October 17th, 2023

#### Talk @ PyTorch conf 2023

Jae-Won gave a talk about energy-efficient ML at the PyTorch conference 2023!

YouTube Slides

- August 14th, 2023

#### ML.ENERGY Colosseum

Two LLMs will battle on your command in terms of both response quality and energy. Your judgement tips the scale of victory.

Colosseum

- July 6th, 2023

#### ML.ENERGY Leaderboard

A rich benchmark and comparison of performing inference on modern LLMs with metrics including energy, latency, and model quality!

Leaderboard Repository

- March 4th, 2023

#### Chase → ICLR Workshop '23

Based on Carbon-Aware Zeus, Chase was accepted to appear at the ICLR workshop on Tackling Climate Change with Machine Learning!

Repository Paper

- November 10th, 2022

#### Zeus in Carbon Hack 22

Carbon-Aware Zeus wins the Second best overall solution prize in CarbonHack22 organized by the Green Software Foundation!

Project page YouTube

- July 8th, 2022

#### Zeus → NSDI '23

Our first project "Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training" was accepted to appear at NSDI '23!

Zeus website Paper

## Sponsors

Any opinions, findings, and conclusions of our works are those of the author(s) and do not necessarily represent the official policy of any of these organizations.