深度学习框架及其发展形势的思考

Abstract:Deep Learning Frameworks serve as a critical bridge between low-level AI hardware and user-facing AI applications by providing essential building blocks for designing，training，and deploying deep learning models. Similar to how operating systems underpin software development in the desktop and mobile era，deep learning frameworks support and drive AI research and innovation. This paper presents a comprehensive survey of the current state of deep learning frameworks，covering their core technologies and ecosystems. It provides an in-depth analysis of the latest trends and challenges in deep learning frameworks，from various perspectives such as industrial applications of AI，hardware-software integration and optimization，recent advances in Large Language Models （LLM），and interdisciplinary frontiers of scientific computing. Furthermore，the paper offers several recommendations to accelerate hardware-software integration for deep learning frameworks，promote the growth of Chinese deep learning frameworks，support cutting-edge initiatives such as AI for Science，and strengthen the Chinese deep learning framework ecosystems.

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