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BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations.

Georgios Smaragdos1, Georgios Chatzikonstantis, Rahul Kukreja

  • 1Neuroscience department, Erasmus MC, Wytemaweg 80, 3015GE, Rotterdam, Netherlands.

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BrainFrame, a heterogeneous computing platform, efficiently handles diverse computational neuroscience models. It integrates multiple accelerators for improved performance and energy efficiency in brain studies.

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Area of Science:

  • Computational Neuroscience
  • High-Performance Computing (HPC)

Background:

  • Computational models are increasingly used in brain studies, demanding significant computational resources.
  • Existing High-Performance Computing (HPC) platforms struggle to meet the diverse requirements of these complex models.
  • A homogeneous acceleration platform is insufficient for the varied needs of computational neuroscience.

Purpose of the Study:

  • To introduce BrainFrame, a novel heterogeneous acceleration platform for computational neuroscience.
  • To evaluate BrainFrame's performance using a complex neuron model of the inferior-olivary nucleus.
  • To demonstrate BrainFrame's ability to handle diverse modeling requirements and optimize performance.

Main Methods:

  • Developed BrainFrame, integrating an Intel Xeon-Phi CPU, NVidia GP-GPU, and Maxeler Dataflow Engine.
  • Integrated the PyNN software framework for model compatibility and usability.
  • Tested performance on extended Hodgkin-Huxley neuron models with varying network dimensions and connectivity densities.

Main Results:

  • BrainFrame effectively manages diverse modeling requirements across different experiment instances.
  • The heterogeneous platform achieves significantly lower energy consumption compared to homogeneous systems.
  • Performance analysis confirmed that all three integrated acceleration technologies are necessary for diverse use cases.

Conclusions:

  • BrainFrame transparently selects appropriate accelerators, enhancing usability for computational neuroscientists.
  • PyNN integration provides access to existing models and a pathway for future platform development.
  • The BrainFrame framework offers a promising solution for wider adoption and advancement in computational neuroscience research.