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

  • Authors:

  • Daniel Brüderle;Mihai A. Petrovici;Bernhard Vogginger;Matthias Ehrlich;Thomas Pfeil;Sebastian Millner;Andreas Grübl;Karsten Wendt;Eric Müller;Marc-Olivier Schwartz;Dan Husmann de Oliveira;Sebastian Jeltsch;Johannes Fieres;Moritz Schilling;Paul Müller;Oliver Breitwieser;Venelin Petkov;Lyle Muller;Andrew P. Davison;Pradeep Krishnamurthy;Jens Kremkow;Mikael Lundqvist;Eilif Muller;Johannes Partzsch;Stefan Scholze;Lukas Zühl;Christian Mayr;Alain Destexhe;Markus Diesmann;Tobias C. Potjans;Anders Lansner;René Schüffny;Johannes Schemmel;Karlheinz Meier

  • Affiliations:

  • Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany and Robotics Innovation Center, DFKI Bremen, Bremen, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Unité de Neuroscience, Information et Complexité, CNRS, Gif sur Yvette, France;Unité de Neuroscience, Information et Complexité, CNRS, Gif sur Yvette, France;Computational Biology, KTH Stockholm, Stockholm, Sweden;University of Freiburg, Bernstein Center Freiburg, Freiburg, Germany;Computational Biology, KTH Stockholm, Stockholm, Sweden;Brain Mind Institute, Ecoles Polytechniques Federales de Lausanne, Lausanne, Switzerland;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Unité de Neuroscience, Information et Complexité, CNRS, Gif sur Yvette, France;RIKEN Brain Science Institute and RIKEN Computational Science Research Program, Wako-shi, Japan and Universität Freiburg, Bernstein Center for Computational Neuroscience. Freiburg, Germany;Institute of Neuroscience and Medicine (INM-6), Research Center Jülich, Jülich, Germany and RIKEN Computational Science Research Program, Wako-shi, Japan;Computational Biology, KTH Stockholm, Stockholm, Sweden;Technische Universität Dresden, Institute of Circuits and Systems, Dresden, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany;Kirchhoff Institute for Physics, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2011

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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.