research-article

Beyond human-level accuracy: computational challenges in deep learning

Authors:

Newsha Ardalani,

Gregory DiamosAuthors Info & Claims

PPoPP '19: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

Pages 1 - 14

https://doi.org/10.1145/3293883.3295710

Published: 16 February 2019 Publication History

Abstract

Deep learning (DL) research yields accuracy and product improvements from both model architecture changes and scale: larger data sets and models, and more computation. For hardware design, it is difficult to predict DL model changes. However, recent prior work shows that as dataset sizes grow, DL model accuracy and model size grow predictably. This paper leverages the prior work to project the dataset and model size growth required to advance DL accuracy beyond human-level, to frontier targets defined by machine learning experts. Datasets will need to grow 33--971×, while models will need to grow 6.6--456× to achieve target accuracies.

We further characterize and project the computational requirements to train these applications at scale. Our characterization reveals an important segmentation of DL training challenges for recurrent neural networks (RNNs) that contrasts with prior studies of deep convolutional networks. RNNs will have comparatively moderate operational intensities and very large memory footprint requirements. In contrast to emerging accelerator designs, large-scale RNN training characteristics suggest designs with significantly larger memory capacity and on-chip caches.

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Index Terms

Beyond human-level accuracy: computational challenges in deep learning
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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cover image ACM Conferences

PPoPP '19: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

February 2019

472 pages

ISBN:9781450362252

DOI:10.1145/3293883

General Chair:
Jeff Hollingsworth
University of Maryland
,
Program Chair:
Idit Keidar
Technion, Israel

Copyright © 2019 ACM.

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Published: 16 February 2019

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February 16 - 20, 2019

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PPoPP '19 Paper Acceptance Rate 29 of 152 submissions, 19%;

Overall Acceptance Rate 230 of 1,014 submissions, 23%

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https://doi.org/10.1371/journal.pone.0316832
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https://doi.org/10.1016/j.eswa.2024.125494
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https://dl.acm.org/doi/10.5555/3692070.3693840
Trihinas DMichael PSymeonides M(2024)Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark AnalysisFuture Internet10.3390/fi1612046816:12(468)Online publication date: 13-Dec-2024
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