Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Computer organization & design: the hardware/software interface
Computer organization & design: the hardware/software interface
Discrete Applied Mathematics
FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks
FPGA implementation of an adaptive stochastic neural model
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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In this paper a FPGA implementation of a novel neural stochastic model for solving constrained NP-hard problems is proposed and developed. The model exploits pseudo-Boolean functions both to express the constraints and to define the cost function, interpreted as energy of a neural network. A wide variety of NP-hard problems falls in the class of problems that can be solved by this model, particularly those having a quadratic pseudo-Boolean penalty function. The proposed hardware implementation provides high computation speed by exploiting parallelism, as the neuron update and the constraint violation check can be performed in parallel over the whole network. 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 featured dramatically faster computation, with respect to the software implementation, even adopting a low-cost FPGA chip.