FPGA Implementation of an Adaptive Stochastic Neural Model

  • Giuliano Grossi
  • Federico Pedersini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)


In this paper a FPGA implementation of a novel neural stochastic model for solving constrained NP-hard problems is proposed and developed. The hardware implementation allows to obtain high computation speed by exploiting parallelism, as the neuron update and the constraint violation check phases can be performed simultaneously.

The neural system has been tested on random and benchmark graphs, showing good performance with respect to the same heuristic for the same problems. Furthermore, the computational speed of the FPGA implementation has been measured and compared to software implementation. The developed architecture features dramatically faster computations with respect to the software implementation, even adopting a low-cost FPGA chip.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Giuliano Grossi
    • 1
  • Federico Pedersini
    • 1
  1. 1.Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano, Via Comelico 39, I-20135 MilanoItaly

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