Performance Evaluation of MindSpore and PyTorch Based on Ascend NPU

The development of deep learning depends on the support of deep learning processing units and frameworks. HUAWEI's Ascend Neural Processing Unit (Ascend NPU), as a chip designed specifically for neural network computation acceleration, not only provides support for its self-developed framework MindSpore, but also provides adaptation for PyTorch. However, there is a lack of comparative evaluation research on MindSpore and other frameworks on Ascend NPU, making it difficult to understand their actual performance. Therefore, comprehensive evaluation experiments in both model level and operator level on MindSpore and PyTorch based on Ascend NPU are conducted in this paper, analyzing and comparing the performance of the two frameworks. With the conclusions analyzed on the model evaluation experiments and operator evaluation experiments, we provide references and suggestions for deep learning researchers and practitioners in terms of framework selection and optimization strategies for Ascend NPU.