DIPVIEW | Linksium
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DIPVIEW

Maturation

Reducing the energy cost/carbon footprint of training deep learning models

profilePhoto   Chloé Poyet
Linksium Contact Chloé Poyet +33 (0)6 46 57 42 17 chloe.poyet@linksium.fr
190047 R2 D2

Benefits

  • Automation of the training parallelization of CNNs
  • Significant acceleration of the training process
  • Decrease in AI frugality

Key words

  • Deep learning
  • Distributed system
  • Applied mathematics
  • Medical applications

Intellectual Property

  • 1 patent
  • 1 software

Laboratory

  • LIG

Institutions

  • CNRS
  • GRENOBLE INP-UGA
  • INRIA
  • UGA

Linksium Continuum

  • Maturation

Context

The objective of the project is the creation of a start-up that aims to offer products and services to optimise and accelerate deep learning on parallel and heterogeneous architectures. This will be achieved by implementing a recommender that reduces the energy cost/carbon footprint of training deep learning models.

Technology

The Dipview project consists of two main components:

  • The Auto-CNNp (Automatic CNN parallelization) framework based on software components that automate the process of training convolutional neural networks (CNNs).
  • A multi-criteria recommender for the ideal configuration of resources for deep learning.

Advantages

  • Automation of the training parallelization of CNNs
  • Significant acceleration of the training process
  • Decrease in AI frugality

State of progress

A proof of concept (POC) of the Auto-CNNp solution on medical applications (COVID,...) has been implemented, evaluated and validated over TensorFlow and Horovod.

Applications

  • Medical for the diagnosis of various pathologies (brain tumours)
  • Industrial Robotics
  • Autonomous cars
  • Training and retraining in deep learning

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