Call for papers

  • Submission deadline (extended): September 1, 2023 (11:59 AM Pacific Time)
  • Author notification: September 15, 2023 (11:59 AM Pacific Time)
  • Camera-ready submission: September 28, 2023 (11:59 AM Pacific Time)


Topics of interest include the following:

  • Developing optimization strategies for reducing the energy consumption in deep learning
  • Design of novel architectures and operators that are suitable for data-intensive scenarios
  • Developing distributed, efficient reinforcement learning algorithms
  • Implementing large-scale pre-training techniques for real-world applications
  • Developing distributed training approaches and architectures
  • Utilizing HPC and massively parallel architectures for deep learning
  • Exploring frameworks and optimization algorithms for training deep networks
  • Utilizing model pruning, gradient compression techniques, and quantization to reduce the computational complexity
  • Developing methods to reduce the memory/data transmission footprint
  • Developing methods and differentiable metrics to estimate computational costs, energy consumption and power consumption of models
  • Designing, implementing and using hardware accelerators for deep learning
  • Developing efficient and cost saving models and methods that promote diversity and inclusivity in the field of deep learning.
  • Speed up of the training and inference of GPT and other generative models

Submission and review

We invite submission of papers describing work in the domains suggested above or in closely-related areas.

Reviewing of the submissions will be double-blind. Accepted submissions will be presented either as oral or posters at the workshop, and published in the BMVC 2023 Workshops proceedings.

All submissions will follow the BMVC style: submitted papers should not exceed NINE pages (references are excluded, but appendices are included). BMVC instructions for authors are available at this link, whereas the template can be directly downloaded from this link.

Submission site

The submission site is available at: