Hopfield neural networks for the scheduling of data flow Petri nets

One of the major problems in parallel architectures conception is the scheduling of the tasks, taking into account the temporal and hardware constraints. Data flow Petri nets (DFPN) are a very powerful tool to model this parallelism. In this paper, we propose a methodology applying the Hopfield neural networks to the DFPN scheduling. In DFPN, the conception of parallel computation algorithms is modelled with a graph which describes the operations set. Thus, the principle is to compute an optimal path in an oriented graph, in order to find the optimal computing time of a program with a limited number of resources. The use of neural networks with feedback connections provides a computing model capable of exploiting fine-grained parallelism to solve a rich class of optimization problems and they can achieve high computation rates by employing a massive number of simple processing elements. We describe the resolution method and show that Hopfield like neural networks are very powerful to compute the scheduling of DFPN