Boosting the execution performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world applications. However, existing approaches to optimizing the tensor computation of DNNs only consider transformations representable by a fixed set of predefined tensor operators, resulting in a highly restricted optimization space.
