A curated list of trustworthy deep learning papers. Daily updating...

🌱 EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.

A curated list of awesome Green AI resources and tools to assess and reduce the environmental impacts of using and deploying AI.

[ACM e-Energy'24] Code and data for "FedZero: Leveraging Renewable Excess Energy in Federated Learning"

Use local Large Language Models (LLMs) while monitoring energy usage.

GATorch is a tool seamlessly integrated with PyTorch that enables ML developers to generate an energy consumption report. By attaching your model, the tool automatically tracks the energy consumption of your model's training and generates graphs and plots to gain in-depth insights into the energy consumption of your model.

A curated list of Green AI resources.

A machine learning pipeline taking you from raw data to fully trained machine learning model - from data to model (d2m).

Adaptive Sparse Training (AST): 92.1% ImageNet-100 accuracy with 61% energy savings and zero degradation. Production-ready implementations for energy-efficient deep learning with ResNet50 and modern architectures.

A Grafana panel to monitor the carbon footprint of AWS, GCP and Azure instances. Maintains a list of data centres and their energy sources, and computes CO2e emissions by sampling energy consumption of CPU, GPU and DRAM.

Mapping the Landscape of AI, ML, and DL in Climate Change Research

Hackathon winner at AI Engineer World Fair Hackathon: Transforming code, one function at a time, to reduce digital carbon footprints and create a more sustainable digital world.

Guidelines to deploy AI models in different cloud providers aligned with green AI goals.

Replication package for the data analysis of the master thesis "An analysis of modeling and training decisions for greener computer vision systems"

Resource-efficient LLM distillation: Improving sustainability and reducing computational costs of Large Language Models in financial analytics through knowledge distillation.

🌱 ELO 2 – Green AI: a collaborative research project exploring efficient and sustainable approaches to open-source language models.

Energy consumption of ML inference with Runtime Engines

Code and Experimental Package attached to the article "Greener Machine Learning Computing with Intel AI Acceleration"

Hex-Ensemble architecture implementation based on Z/6Z Modular Isomorphism. Proposes 'Shared-Nothing' NPUs to overcome Von Neumann bottlenecks, achieving a +24% Inverse Generalization Gap and high energy efficiency.