FPGA Implementation of an Adaptive Stochastic Neural Model
- Cite this paper as:
- Grossi G., Pedersini F. (2007) FPGA Implementation of an Adaptive Stochastic Neural Model. In: de Sá J.M., Alexandre L.A., Duch W., Mandic D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg
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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