Simulating Execution Time of Tensor Programs using Graph Neural Networks

Understand

Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration.

• Searching the configuration space exhaustively is typically infeasible in practice.
• In line with recent research using TVM, we propose to learn a surrogate model to overcome this issue.
• The model is trained on an acyclic graph called an abstract syntax tree, and utilizes a graph convolutional network to exploit structure in the graph.