General neural computer architecture and its ANN-based task assignment method for parallel-distributed processing

A new DSP-based neural simulating computer architecture and its ANN-based assignment method for parallel distributed processing are proposed. The hardware of the proposed neural simulating computer can be reconfigured in terms of a variety of research interests and requirements of pattern recognition. The software programming environment utilizes an intelligent compiler to perform static task assignment in both the cases of single-task muliprocessor and multitask processor. An improved Hopfield neural network which can converge to global optical solution is employed by the compiler to map different tasks or neurons to their corresponding real processors. An approach of introducing hidden layer to increase the computation ability of the neural simulating computer is also developed. Finally, a proof is given which shows that the use of improved Hopfield algorithm and the modification to network structure doesn't change the intrinsic properties of the original network.