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Evaluation and Prediction of Resource Usage for multi-parametric Deep Learning training and inference

Published: 14 February 2024 Publication History

Abstract

Deep learning is increasingly used in diverse application fields with results typically surpassing those of traditional machine learning techniques. The portfolio of available neural networks is wide, consisting of the full range in terms of complexity, from compact networks to large ones with multiple layers and parameters. This heterogeneity in the model topology is reflected, not necessarily linearly, on the required computational resources for training and inference. Similarly, the environments where the neural networks are trained and executed are transformed from fully-fledged centralized nodes to distributed architectures with constrained resources. In this view, computational resource requirements can be one of the criteria for resource usage management and neural network selection. In this work we measure the training times for a set of five convolutional neural networks of varying complexity and age (GoogleNet, ShuffleNet, VGGish, YAMNet) under different training configurations, considering the batch size, the number of epochs and the learning rate. These measurements are used to create a CPU-time-training dataset of more than 500 values. This dataset is used to train and evaluate models, based on neural networks, for estimating and predicting training times depending on the models employed and the training parameters. Five regression models have been trained and evaluated in terms of correlation coefficient and root mean square error. In addition, we measure the CPU times needed for inference for a subset of the trained models, which prove to be uncorrelated with the corresponding training times.

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cover image ACM Other conferences
PCI '23: Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics
November 2023
304 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 14 February 2024

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Author Tags

  1. Classification and regression trees
  2. Computing methodologies
  3. Machine learning
  4. Machine learning approaches

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PCI 2023

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Overall Acceptance Rate 190 of 390 submissions, 49%

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