A comprehensive workflow for general-purpose
neural modeling with highly configurable
neuromorphic hardware systems.
Brüderle D, Petrovici MA, Vogginger B, Ehrlich M, Pfeil T, Millner
S, Grübl A, Wendt K, Müller E, Schwartz MO, de Oliveira DH,
Jeltsch S, Fieres J, Schilling M, Müller P, Breitwieser O, Petkov V,
Muller L, Davison AP, Krishnamurthy P, Kremkow J, Lundqvist M,
Muller E, Partzsch J, Scholze S, Zühl L, Mayr C, Destexhe A,
Diesmann M, Potjans TC, Lansner A, Schüffny R, Schemmel J, Meier
K.
Biological Cybernetics 104: 263-296, 2011.
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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