Learning Convolutional Neural Networks for Data-Flow Graph Mapping on Spatial Programmable Architectures (Abstract Only)

Data flow graph (DFG) mapping is critical for the compiling of spatial programmable architecture, where compilation time is a key factor for both time-to-market requirement and mapping successful rate. Inspired from the great progress made in tree search game using deep neural network, we proposed a framework for learning convolutional neural networks for mapping DFGs onto spatial programmable architectures. Considering that mapping is a process from source to target, we present a dual-input neural network capturing features from both DFGs in applications and Process Element Array (PEA) in spatial programmable architectures. In order to train the neural network, algorithms are designed to automatically generate a data set from PEA intermediate states of preprocessed DFG. Finally, we demonstrate that the trained neural network can get high identifying accuracy of mapping quality and our proposed mapping approach are competitive with state-of-the-art DFG mapping algorithms in performance while the compilation time is greatly reduced.