A neural network based algorithm for the scheduling problem in high-level synthesis

Abstract

A new scheduling approach for high-level synthesis based on a deterministic modified Hopfield model is presented. The model uses a four-dimensional neural network architecture to schedule the operations of a data flow graph (DFG), and maps them to specific functional units. Neural network-based scheduling (NNS) is achieved by formulating the scheduling problem in terms of an energy function, and by using the motion equation corresponding to the variation of energy. The algorithm searches the scheduling space in parallel and finds the optimal schedule. This yields an efficient parallel scheduling algorithm under time and resource constraints appropriate for implementing on a parallel machine. The algorithm is based on moves in the scheduling space, which correspond to moves towards the equilibrium point (lowest energy state) in the dynamic system space.<>