A framework for measuring, tracking and reduction of carbon emissions from large deep learning models - BC-934

Genre de projet: Innovation
Discipline(s) souhaitée(s): Génie - informatique / électrique, Génie, Informatique, Sciences mathématiques, Mathématiques
Entreprise: ALSGeoAnalytics
Durée du projet: 4 à 6 mois
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): Vancouver, BC, Canada
Nombre de postes: 1
Niveau de scolarité désiré: Études de premier cycle/baccalauréatMaîtrise
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

ALS’ Geoanalytics services integrate data science, cutting-edge AI technology and geoscience expertise to help you eliminate manual processes and transform your data into valuable insights. Our comprehensive services empower mining and exploration managers and technical experts with real-time decision-making for improved output and faster time-to-market.

Veuillez décrire le projet.: 

This project aims to develop an innovative framework to measure, track, and reduce the carbon footprint of large deep learning models. As the adoption of artificial intelligence (AI) grows, the energy consumption and associated emissions from training and deploying large-scale models have become a significant environmental concern. This research and innovation project addresses the pressing need for sustainable AI practices by delivering tools and methodologies to quantify and minimize the environmental impact of machine learning (ML).

The project’s ultimate goal is to deliver a comprehensive methodology and software tool that allows organizations to quantify and minimize the carbon emissions of their AI workflows.

The project’s deliverables include:
• Carbon Emission Tracking Framework: A robust system to monitor and measure the energy consumption and carbon emissions associated with ML workflows.
• Optimization Strategies: A suite of energy-efficient algorithmic enhancements and workflows for reducing emissions during training and deployment.
• Actionable Insights and Software Tools: An intuitive software platform that not only tracks emissions but provides tailored recommendations for carbon reduction.
• Validation and Benchmarks: A comprehensive evaluation of the framework’s effectiveness against real-world AI models and industry-standard benchmarks.

The innovation lies in the development of a multi-faceted approach combining data-driven models with real-world energy metrics to provide actionable and scalable solutions. Tasks for the research candidate include developing carbon footprint tracking systems, optimizing training pipelines for energy efficiency, and validating the framework against state-of-the-art benchmarks

This project bridges the gap between technological innovation and environmental responsibility and offers practical, actionable solutions for industry-wide adoption, making it a valuable resource for achieving a more energy efficient future.

Expertise ou compétences exigées: 

• Knowledge on Machine Learning (ML), Deep Learning (DL).
• Proficieny in programming in Python language, and expertise in working with Python ML frameworks such as scikit-learn and PyTorch.
• Knowledge of Restful APIs.
• Knowledge of Databases is an asset.
• Knowledge of React is an asset.