How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts

• URL: http://arxiv.org/abs/2412.17376v1
• Date: Mon, 23 Dec 2024 08:24:44 GMT
• Title: How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts
• Authors: Clément Morand, Anne-Laure Ligozat, Aurélie Névéol,
• Abstract summary: optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI. This paper investigates the evolution of individual graphics cards production impacts and of the environmental impacts associated with training Machine Learning (ML) models over time.
• Score: 2.640490842167383
• License:
• Abstract: The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. This paper investigates the evolution of individual graphics cards production impacts and of the environmental impacts associated with training Machine Learning (ML) models over time. We collect information on graphics cards used to train ML models and released between 2013 and 2023. We assess the environmental impacts associated with the production of each card to visualize the trends on the same period. Then, using information on notable AI systems from the Epoch AI dataset we assess the environmental impacts associated with training each system. The environmental impacts of graphics cards production have increased continuously. The energy consumption and environmental impacts associated with training models have increased exponentially, even when considering reduction strategies such as location shifting to places with less carbon intensive electricity mixes. These results suggest that current impact reduction strategies cannot curb the growth in the environmental impacts of AI. This is consistent with rebound effect, where the efficiency increases fuel the creation of even larger models thereby cancelling the potential impact reduction. Furthermore, these results highlight the importance of considering the impacts of hardware over the entire life-cycle rather than the sole usage phase in order to avoid impact shifting. The environmental impact of AI cannot be reduced without reducing AI activities as well as increasing efficiency.

Related papers

• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  Much of this debate has concentrated on direct impact without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts [0.0]
  We propose a methodology to estimate the environmental impact of a company's AI portfolio. Results confirm that large generative AI models consume up to 4600x more energy than traditional models. Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain.
  arXiv  Detail & Related papers  (2025-01-24T08:58:49Z)
• Impact of ML Optimization Tactics on Greener Pre-Trained ML Models [46.78148962732881]
  This study aims to (i) analyze image classification datasets and pre-trained models, (ii) improve inference efficiency by comparing optimized and non-optimized models, and (iii) assess the economic impact of the optimizations. We conduct a controlled experiment to evaluate the impact of various PyTorch optimization techniques (dynamic quantization, torch.compile, local pruning, and global pruning) to 42 Hugging Face models for image classification. Dynamic quantization demonstrates significant reductions in inference time and energy consumption, making it highly suitable for large-scale systems.
  arXiv  Detail & Related papers  (2024-09-19T16:23:03Z)
• Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior [0.0]
  surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance. behavioral analysis and mining usage patterns of new energy vehicles can be identified. Environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment.
  arXiv  Detail & Related papers  (2024-06-06T14:03:52Z)
• Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages [56.98243487769916]
  Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning. We propose Adaptive RR, which dynamically adjusts the replay ratio based on the critic's plasticity level.
  arXiv  Detail & Related papers  (2023-10-11T12:05:34Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning [54.409634256153154]
  In Reinforcement Learning (RL), enhancing sample efficiency is crucial. In principle, off-policy RL algorithms can improve sample efficiency by allowing multiple updates per environment interaction. Our study investigates the underlying causes of this phenomenon by dividing plasticity into two aspects.
  arXiv  Detail & Related papers  (2023-06-19T06:14:51Z)
• Sustainable AI: Environmental Implications, Challenges and Opportunities [13.089123643565724]
  We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases. We present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI.
  arXiv  Detail & Related papers  (2021-10-30T23:36:10Z)
• Compute and Energy Consumption Trends in Deep Learning Inference [67.32875669386488]
  We study relevant models in the areas of computer vision and natural language processing. For a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated.
  arXiv  Detail & Related papers  (2021-09-12T09:40:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.