An approach to a sequential-like parallel algorithm in a Boltzmann machine

The efficient implementation of the neural network is a key task in looking for a high speed algorithm. In this paper, we address the problem of optimizing sequential and parallel algorithms for the Boltzmann Machine (BM). We present a novel parallel algorithm similar to the sequential one in the operational results and suitable for parallel hardware implementation of a BM. Since the algorithm performance depends on the probability of the accepted state transition in the annealing process, we increase the the rate of the state change to enhance this probability. In addition, we give the mathematical function describing the rate of the state change, provide experimental data on a well-known optimization problem TSP to have a verification of the function and show that the proposed algorithm obtains much more speedup in comparison with the traditional algorithm