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A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

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Abstract

In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware–software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

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Author notes

  1. Moritz Schilling

    Present address: Robotics Innovation Center, DFKI Bremen, Bremen, Germany

Authors and Affiliations

  1. Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

    Daniel Brüderle, Mihai A. Petrovici, Bernhard Vogginger, Thomas Pfeil, Sebastian Millner, Andreas Grübl, Eric Müller, Marc-Olivier Schwartz, Dan Husmann de Oliveira, Sebastian Jeltsch, Johannes Fieres, Moritz Schilling, Paul Müller, Oliver Breitwieser, Venelin Petkov, Johannes Schemmel & Karlheinz Meier

  2. Unité de Neuroscience, Information et Complexité, CNRS, Gif sur Yvette, France

    Lyle Muller, Andrew P. Davison & Alain Destexhe

  3. RIKEN Brain Science Institute and RIKEN Computational Science Research Program, Wako-shi, Japan

    Markus Diesmann

  4. Bernstein Center for Computational Neuroscience, Universität Freiburg, Freiburg, Germany

    Markus Diesmann

  5. Institute of Circuits and Systems, Technische Universität Dresden, Dresden, Germany

    Matthias Ehrlich, Karsten Wendt, Johannes Partzsch, Stefan Scholze, Lukas Zühl, Christian Mayr & René Schüffny

  6. Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany

    Jens Kremkow

  7. Computational Biology, KTH Stockholm, Stockholm, Sweden

    Pradeep Krishnamurthy, Mikael Lundqvist & Anders Lansner

  8. Brain Mind Institute, Ecoles Polytechniques Federales de Lausanne, Lausanne, Switzerland

    Eilif Muller

  9. Institute of Neuroscience and Medicine (INM-6), Research Center Jülich, Jülich, Germany

    Tobias C. Potjans

  10. RIKEN Computational Science Research Program, Wako-shi, Japan

    Tobias C. Potjans

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  1. Daniel Brüderle
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  31. Anders Lansner
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Correspondence to Daniel Brüderle.

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Brüderle, D., Petrovici, M.A., Vogginger, B. et al. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern 104, 263–296 (2011). https://doi.org/10.1007/s00422-011-0435-9

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