Effective mapping of artificial neural network algorithms onto massively parallel hardware: the REMAP programming environment

Abstract

The application of artificial neural networks (ANN) in real-time embedded systems demands high performance computers. Miniaturized massively parallel architectures are suitable computation platforms for this task. An important question which arises is how to establish an effective mapping from ANN algorithms to hardware. In this paper, we demonstrate how an effective mapping can be achieved with our programming environment in close combination with an optimized architecture design targeted for neuro-computing.<>