A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems
2011 (English)In: Biological cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 104, no 4-5, 263-296 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2011. Vol. 104, no 4-5, 263-296 p.
Neuromorphic, VLSI, Hardware, Wafer scale, Software, Modeling, Computational neuroscience, PyNN
IdentifiersURN: urn:nbn:se:kth:diva-35146DOI: 10.1007/s00422-011-0435-9ISI: 000291354900004ScopusID: 2-s2.0-80052931593OAI: oai:DiVA.org:kth-35146DiVA: diva2:426025
FunderSwedish e‐Science Research Center
QC 201106222011-06-222011-06-202012-05-23Bibliographically approved