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.>

Multiple-input maximum and minimum circuits in current mode are proposed. The operation of these circuits is formulated using simultaneous bounded-difference equations. The exact analyses are performed by solving the bounded-difference equations. The accuracy of these circuits is better than the accuracy of binary tree realizations using two-input max/min circuits because no accumulation of errors occurs; furthermore, the operation speed is higher than the speed of the binary tree realization. The proposed circuits consist of only MOS transistors and are compatible with standard MOS fabrication processes. These circuits are useful building blocks for a real-time fuzzy controller and a fuzzy computer.>