Implementation of efficient, scalable deep learning models Scalable Machine Learning

Description

BACKGROUND

Event-based neural network models are biologically inspired [1] and have the ability to scale well [2] while being extremely energy efficient on neuromorphic hardware [5].

YOUR TASK

Create a high-performance implementation of event-based models based on [6]. This can be using one of the following

  1. Rust or Elixir
  2. As an extension of GeNN
  3. Using MPI in C++
  4. For the Graphcore AI chip.

Required Skills

  • Good Knowledge of Rust/Elixir/C++/CUDA depending on the choice of implementation.
  • Knowledge of concurrent programming concepts.
  • Basic Knowledge in deep learning.

References

[1] Pfeiffer, Michael, and Thomas Pfeil. "Deep learning with spiking neurons: opportunities and challenges." Frontiers in neuroscience (2018): 774. (link)
[2] Jordan, J., Ippen, T., Helias, M., Kitayama, I., Sato, M., Igarashi, J., Diesmann, M. and Kunkel, S., 2018. Extremely scalable spiking neuronal network simulation code: from laptops to exascale computers. Frontiers in neuroinformatics, p.2. (link)
[4] GeNN, a GPU-enhanced Neuronal Network simulation
[5] Christensen, Dennis Valbjørn, et al. "2022 roadmap on neuromorphic computing and engineering." Neuromorphic Computing and Engineering (2022).
[6] Subramoney, A., Nazeer, K.K., Schöne, M., Mayr, C., Kappel, D., 2022. EGRU: Event-based GRU for activity-sparse inference and learning. https://doi.org/10.48550/arXiv.2206.06178

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210