Watt For What: Rethinking Deep Learning's Energy-Performance Relationship

• URL: http://arxiv.org/abs/2310.06522v2
• Date: Tue, 17 Sep 2024 14:30:04 GMT
• Title: Watt For What: Rethinking Deep Learning's Energy-Performance Relationship
• Authors: Shreyank N Gowda, Xinyue Hao, Gen Li, Shashank Narayana Gowda, Xiaobo Jin, Laura Sevilla-Lara,
• Abstract summary: We study the trade-off between model accuracy and electricity consumption of deep learning models. By evaluating accuracy per unit of electricity consumed, we demonstrate how smaller, more energy-efficient models can significantly expedite research. This research contributes to a more equitable research landscape, where smaller entities can compete effectively with larger counterparts.
• Score: 13.505163099299025
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy. However, their increasing energy consumption has raised concerns about their environmental impact, disadvantaging smaller entities in research and exacerbating global energy consumption. In this paper, we explore the trade-off between model accuracy and electricity consumption, proposing a metric that penalizes large consumption of electricity. We conduct a comprehensive study on the electricity consumption of various deep learning models across different GPUs, presenting a detailed analysis of their accuracy-efficiency trade-offs. By evaluating accuracy per unit of electricity consumed, we demonstrate how smaller, more energy-efficient models can significantly expedite research while mitigating environmental concerns. Our results highlight the potential for a more sustainable approach to deep learning, emphasizing the importance of optimizing models for efficiency. This research also contributes to a more equitable research landscape, where smaller entities can compete effectively with larger counterparts. This advocates for the adoption of efficient deep learning practices to reduce electricity consumption, safeguarding the environment for future generations whilst also helping ensure a fairer competitive landscape.

Related papers

• Just In Time Transformers [2.7350304370706797]
  JITtrans is a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems.
  arXiv  Detail & Related papers  (2024-10-22T10:33:00Z)
• Towards Physical Plausibility in Neuroevolution Systems [0.276240219662896]
  The increasing usage of Artificial Intelligence (AI) models, especially Deep Neural Networks (DNNs), is increasing the power consumption during training and inference. This work addresses the growing energy consumption problem in Machine Learning (ML) Even a slight reduction in power usage can lead to significant energy savings, benefiting users, companies, and the environment.
  arXiv  Detail & Related papers  (2024-01-31T10:54:34Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• 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)
• Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI [8.025202812165412]
  We consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective. We highlight innovative and promising energy-efficient practices for training deep learning models.
  arXiv  Detail & Related papers  (2023-03-24T12:48:21Z)
• Energy Efficiency of Training Neural Network Architectures: An Empirical Study [11.325530936177493]
  The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The computations needed to train such models entail a large carbon footprint. We study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$$ emissions produced during training.
  arXiv  Detail & Related papers  (2023-02-02T09:20:54Z)
• 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)
• Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision [31.781943982148025]
  We present the first large-scale energy consumption benchmark for efficient computer vision models. A new metric is proposed to explicitly evaluate the full-cycle energy consumption under different model usage intensity.
  arXiv  Detail & Related papers  (2021-08-30T18:22:36Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

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.