A Platform for Parallel Operation of VLSI Neural Networks

نویسندگان

  • J. Fieres
  • A. Grübl
  • S. Philipp
  • K. Meier
  • J. Schemmel
  • F. Schürmann
چکیده

This paper presents a platform for the parallel operation of VLSI neural networks allowing to seamlessly map neural network topologies on distributed resources. The scalable approach provides fast isochronous communication channels transporting the neuron signals between single network modules. The network modules are printed circuit boards hosting a programmable logic with an embedded microprocessor core, memory, and a VLSI neural network ASIC. Currently, the modules are equipped with a mixed-signal neural network ASIC. For its McCulloch-Pitts type neurons a biologically inspired higher-level model of perception is adopted and demonstrated.

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تاریخ انتشار 2004