Neural networks for multiprocessor real-time scheduling

Abstract

In recent years, neural networks have become a popular area of research, especially after Hopfield and Tank opened the way for using neural networks for optimization purposes and surprised the scientific community by their paper (Biological Cybernetics, vol. 52, pp. 141-52, 1985) presenting a circuit to give approximate solutions for the classical traveling salesman problem in a few elapsed propagation times of analog amplifiers. In this paper, we analyze Hopfield neural networks from the scheduling viewpoint to see if they can be used to solve real-time scheduling problems. We build a neural network whose topology depends on real-time task constraints, and converges to an approximate solution of the scheduling problem. Finally, we analyze the quality of the result in terms of the convergence rate and the complexity of the algorithm.<>