The Green Algorithms calculator is an open-access online tool designed to estimate and report the carbon footprint of computational tasks and AI models. Developed by researchers at the University of Cambridge, the initiative addresses the growing, yet often overlooked, environmental impact of modern computing, ranging from high-performance scientific simulations to AI models and big data analytics. It can be used during the planning phase to estimate environmental impacts, or retrospectively for accounting and monitoring.

Zeus is an open source library for measuring and optimising the energy consumption of deep learning workloads. It records the energy and power used by training and inference across a range of GPUs, CPUs and other hardware. It also adjusts settings such as GPU power limits, batch size and frequency to reduce energy use while balancing the trade off with training time, supporting efforts towards more environmentally sustainable AI development.

The Agentic Benchmark for CRM is a benchmarking framework developed by Salesforce to evaluate the performance of AI agents and models in enterprise customer relationship management (CRM) use cases using metrics such as accuracy, cost, speed, trust and safety, and sustainability.

AI Energy Score is an initiative to establish standardized energy efficiency ratings for AI models in order to help the industry make informed decisions about sustainability in AI development.

Online tool for estimating the carbon emissions generated by AI model usage.

This Recommendation describes specifications of a data centre infrastructure management (DCIM) system based on big data and artificial intelligence (AI) technology.

Evaluating deep learning software frameworks to help manufactures take full advantage of certain frameworks and avoid the disadvantages of others.

This guidance document is intended to support machine learning (ML) researchers and operators to measure and improve the environmental efficiency of ML, artificial intelligence (AI) and other emerging technologies use in supply chain management.

Formal methods for the performance benchmarking for AI server systems are provided in this standard, including approaches for test, metrics, and measure

This standard specifies a framework for adding artificial intelligence (AI) functions to support the energy management agent (EMA) specified in ISO/IEC for EMAs located on customer premises.

This document specifies Neural Network Coding (NNC) as a compressed representation of the parameters/weights of a trained neural network and a decoding process for the compressed representation, complementing the description of the network topology in existing (exchange) formats for neural networks.

Recommendation ITU-T M.3381 provides requirements for energy saving management of a 5G radio access network (RAN) system with artificial intelligence (AI).

This Supplement aims to investigate appropriate models to evaluate urban energy efficiency with a special focus on the emerging adoption of AI and big data.

This tool examines energy-saving technology for fifth generation (5G) base stations (BSs).

Code for our nips19 paper: You Only Propagate Once: Accelerating Adversarial Training Via Maximal Principle

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

JAX implementation of OpenAI's Whisper model for up to 70x speed-up on TPU.

NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.

Spring 2018 - 10.009 Digital World 1D Project

High-level Deep Learning Framework written in Kotlin and inspired by Keras