High-performance software implementation of event-based deep learning models Theory of Neural Systems

Description

BACKGROUND

Event-based neural network models such as biologically inspired spiking neural networks [1] have the ability to scale well [2] while being extremely energy efficient on neuromorphic hardware [5]. While many efficient implementations exist [3,4], few support learning algorithms common in the deep learning community such as backpropagation.

YOUR TASK

Create a high-performance implementation of event-based models in an appropriate language such as Rust/Elixir or using CUDA. These implementations will also support standard learning algorithms from deep learning.

Required Skills

  • Good Knowledge of Rust/Elixir/C++ and/or CUDA
  • 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).

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.

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