Abstract: A processor-implemented method of pre-processing for deep neural network compilation comprising receiving a representation of an artificial neural network (ANN) model. An operator embedding is generated to represent operators of the ANN model in an embedding space. A graph neural network (GNN) processes the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric. The GNN determines a set of hyperparameters for the ANN model based on the graph embedding.

Abstract: A processor-implemented method of pre-processing for deep neural network compilation comprising receiving a representation of an artificial neural network (ANN) model. The ANN includes multiple nodes coupled by edges. Position information is determined for each node of the ANN. An operator embedding is generated to represent operators of the ANN model in an embedding space based on the position information. A graph neural network (GNN) processes the operator embedding to generate a graph embedding corresponding to the ANN model according to a learned distance metric and based on the position information. The GNN determines a set of hyperparameters for the ANN model based on the graph embedding.