Energy Efficiency of Training Neural Network Architectures: An Empirical Study

• URL: http://arxiv.org/abs/2302.00967v1
• Date: Thu, 2 Feb 2023 09:20:54 GMT
• Title: Energy Efficiency of Training Neural Network Architectures: An Empirical Study
• Authors: Yinlena Xu, Silverio Mart\'inez-Fern\'andez, Matias Martinez, and Xavier Franch
• Abstract summary: 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.
• Score: 11.325530936177493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$_2$ emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) the trade-off between accuracy and energy efficiency; and (iii) the difference on the method of measurement of the energy consumed using software-based and hardware-based tools.

Related papers

• 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)
• From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems [9.658615045493734]
  We study several neural network architectures that are key components of sound event detection systems. We measure the energy consumption for training and testing small to large architectures. We establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.
  arXiv  Detail & Related papers  (2024-09-08T12:51:34Z)
• ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation [4.77407121905745]
  Back-propagation (BP) is a major source of computational expense during training deep learning models. We propose a general, energy-efficient convolution module that can be seamlessly integrated into any deep learning architecture.
  arXiv  Detail & Related papers  (2024-08-22T17:22:59Z)
• 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)
• Do DL models and training environments have an impact on energy consumption? [9.373015995433589]
  This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models. We show that selecting the proper model architecture and training environment can reduce energy consumption dramatically.
  arXiv  Detail & Related papers  (2023-07-07T12:07:59Z)
• Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training [26.438415753870917]
  We propose EAT, a gradient-based algorithm that aims to reduce energy consumption during model training. We demonstrate that our energy-aware training algorithm EAT is able to train networks with a better trade-off between classification performance and energy efficiency.
  arXiv  Detail & Related papers  (2023-07-01T15:44:01Z)
• 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)
• Energy Transformer [64.22957136952725]
  Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function.
  arXiv  Detail & Related papers  (2023-02-14T18:51:22Z)
• Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration [130.89746032163106]
  We propose ALOE, a new algorithm for learning conditional and unconditional EBMs for discrete structured data. We show that the energy function and sampler can be trained efficiently via a new variational form of power iteration. We present an energy model guided fuzzer for software testing that achieves comparable performance to well engineered fuzzing engines like libfuzzer.
  arXiv  Detail & Related papers  (2020-11-10T19:31:29Z)
• 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.