陈云霁.深度学习处理器：人工智能芯片基础研究之路[J].中国科学院院刊,2026,41(6):1276-1288.

深度学习处理器：人工智能芯片基础研究之路

Deep learning processor: Road to fundamental research on AI chip

陈云霁
中国科学院计算技术研究所处理器芯片全国重点实验室 北京 100190
CHEN Yunji
State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

         深度学习处理器;基础研究;处理器体系结构

        deep learning processor chip;fundamental research;processor architecture

        深度学习处理器芯片是人工智能产业的算力底座，亦是当前国际科技竞争的核心焦点。中国科学院计算技术研究所（以下简称“计算所”）于2008年启动该方向基础研究时，该方向尚未进入国际主流视野，其学术价值与发展规律尚未形成广泛共识，面临意义界定、底层规律、体系架构三大基础科学问题的挑战。针对上述问题，计算所团队开展了系统性探索，实现了上述三大问题的突破，构建起深度学习处理器新范式，并研制出国际首款深度学习处理器芯片寒武纪1号，完成了从理论到工程的初步转化。相关成果推动该方向从无到有成为国际重要研究方向，被六大洲、80余个国家、近2 000家机构广泛引用和应用，也是此后英伟达TensorCore、谷歌TPU、华为NPU等各种产品的先声。文章从研究者与科研管理者的双重维度，系统总结了深度学习处理器基础研究的经验与启示，为中国基础研究推动国际学术与产业变革提供些许参考。

        Chinese Academy of Sciences, Beijing 100190, China） Deep learning processor chips serve as an important computation foundation for the artificial intelligence industry, thus has attracted widespread attention from the international communities. The Institute of Computing Technology, Chinese Academy of Sciences took the lead in initiating fundamental research on deep learning processor chips in 2008. At that time, this research direction had not yet entered the mainstream vision of the international communities, with its academic value and development laws yet to gain widespread recognition, and it was confronted with the challenges of three fundamental scientific issues concerning its significance, principle, and architecture. To address the above issues, the research team carried out systematic exploration, made initial breakthroughs in three aspects, constructed a new paradigm for deep learning processor chips, and developed Cambricon-1, which is the world’s first deep learning processor chips, thus initially completing the preliminary transformation from theory to engineering. These efforts have contributed, in a modest but meaningful way, to the establishment and growth of deep learning processor chip research as an internationally recognized direction. These related works have been widely cited in academia and adopted in industry by nearly 2 000 institutions across 80 countries on six continents. They are earlier than various products for dedicated deep learning acceleration, including NVIDIA’s Tensor Core, Google’s TPU, and Huawei’s NPU. This study systematically summarizes the practical experience and in-depth enlightenment of basic research on deep learning processor chips from the dual perspectives of researchers and research managers, aiming to provide some useful reference for Chinese fundamental research in promoting international academic and industrial progresses.