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GB/T 45280-2025: Artificial intelligence - Unified interfaces for heterogeneous artificial intelligence accelerating units
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Artificial intelligence - Unified interfaces for heterogeneous artificial intelligence accelerating units
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Basic data | Standard ID | GB/T 45280-2025 (GB/T45280-2025) | | Description (Translated English) | Artificial intelligence - Unified interfaces for heterogeneous artificial intelligence accelerating units | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L70 | | Classification of International Standard | 35.020 | | Word Count Estimation | 94,952 | | Date of Issue | 2025-02-28 | | Date of Implementation | 2025-02-28 | | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration | GB/T 45280-2025: Artificial intelligence - Unified interfaces for heterogeneous artificial intelligence accelerating units
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GB/T 45280-2025 English version. Artificial intelligence - Unified interfaces for heterogeneous artificial intelligence accelerating units
ICS 35.020
CCSL70
National Standard of the People's Republic of China
Artificial Intelligence Heterogeneous Artificial Intelligence
Accelerator Unified Interface
intelligence accelerating units
Released on 2025-02-28
2025-02-28 Implementation
State Administration for Market Regulation
The National Standardization Administration issued
Table of Contents
Preface III
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
4 Abbreviations 2
5 Overview 2
5.1 Interface Function 2
5.2 Architecture 2
5.3 Basic Concepts 3
6 Access Method 4
6.1 Accelerator 4
6.2 Machine Learning Framework 4
6.3 Operation process description 4
7 Interface Requirements 5
7.1 Interface Execution Status 5
7.2 Interface Parameters 5
7.3 Accuracy 5
7.4 Enumeration 5
8 Interface Definition 6
8.1 Computational Graph Representation Interface 6
8.2 Runtime Interface 18
8.3 Operator Representation Interface 44
9 Compliance test methods 46
9.1 General Principles46
9.2 Testing Process 47
9.3 Indicators and measurement methods 48
Appendix A (Normative) Return Code 51
Appendix B (Normative) Enumeration 54
Appendix C (Normative) Operator Definition 57
Appendix D (Informative) Domain Interface 78
Appendix E (Informative) Interface Example 83
Appendix F (Normative) Test Item 85
References 87
Foreword
This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents"
Drafting.
Please note that some of the contents of this document may involve patents. The issuing organization of this document does not assume the responsibility for identifying patents.
This document was proposed and coordinated by the National Technical Committee for Information Technology Standardization (SAC/TC28).
This document was drafted by. China Electronics Technology Standardization Institute, Huawei Technologies Co., Ltd., Shanghai Artificial Intelligence Innovation Center, Beijing Aerospace
University of Aeronautics and Astronautics, Shanghai Enflame Technology Co., Ltd., Beijing BiRen Technology Development Co., Ltd., Shanghai Tianshu Zhixin Semiconductor Co., Ltd.,
Cambrian Technologies Co., Ltd., Intel (China) Co., Ltd., Shenzhen Intellifusion Technology Co., Ltd., Muxi Integrated Circuit Co., Ltd.
Road (Shanghai) Co., Ltd., Institute of Software, Chinese Academy of Sciences, Inspur Electronic Information Industry Co., Ltd., Peking University, Shanghai Artificial Intelligence
Energy Industry Association, Huawei Cloud Computing Technology Co., Ltd., Shanghai SenseTime Intelligent Technology Co., Ltd., Beijing Zhixin Microelectronics Technology Co., Ltd.,
Pingtouge (Shanghai) Semiconductor Technology Co., Ltd., Hangzhou Hikvision Digital Technology Co., Ltd., China Mobile Communications Group Co., Ltd.
Shenzhen Kunyun Information Technology Co., Ltd., Loongson Technology Co., Ltd., Nanjing NARI Ruiteng Technology Co., Ltd., Suzhou Deng
Lin Technology Co., Ltd., China Southern Power Grid Artificial Intelligence Technology Co., Ltd., Midea Group (Shanghai) Co., Ltd., Peking University Changsha Computing and
Digital Economy Research Institute, Beijing University of Aeronautics and Astronautics Hangzhou Innovation Research Institute, Sichuan Huakun Zhenyu Intelligent Technology Co., Ltd., Shandong Inspur Technology
China Southern Power Grid Co., Ltd., Southwest University of Science and Technology, Zhejiang Dahua Technology Co., Ltd., Beijing Greenland
Shenzhen Tong Information Technology Co., Ltd. and Beijing Electronic Digital Technology Co., Ltd.
The main drafters of this document are. Dong Jian, Yang Yuze, Zhang Yali, Xu Yang, Zhang Chengxing, Bao Wei, Liu Wenfeng, Pei Zhilin, Wang Waner, Cao Xiaoqi,
Ma Chenghao, Luan Zhongzhi, Mei Jingqing, Ding Ruiquan, Hu Mingshan, Cheng Guipeng, Wang Haining, Su Lan, Liu Zi, Meng Lingzhong, Ma Shanshan, Li Binbin, Su Dongdong,
Yang Chao, Zhao Chunhao, Liu Yong, Zhong Pu, Zhang Yibo, Zhang Fang, Jin Di, Cai Quanxiong, Ma Wanyue, Shi Chao, Ci Hongbin, Chen Rouyi, Cai Yasen, Jia Mengzhu,
Hu Zheng-hui, Zhao Yan-jun, Li Rui, Zhang Xi-ming, Xu Huan, Yu Wen-xin, Fang Gui-ming, Yu Jie, Guo Wen.
Artificial Intelligence Heterogeneous Artificial Intelligence
Accelerator Unified Interface
1 Scope
This document defines the unified interface of heterogeneous artificial intelligence accelerators, its semantics and usage, and describes the functions of each accelerator to implement this interface.
The access methods and test methods required for the port.
This document is applicable to the design and implementation of artificial intelligence accelerator interfaces, and can also provide a reference for artificial intelligence accelerator applications.
2 Normative references
The contents of the following documents constitute the essential clauses of this document through normative references in this document.
For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to
This document.
GB/T 41867-2022 Information Technology Artificial Intelligence Terminology
GB/T 45087-2024 Artificial Intelligence Server System Performance Test Method
GB/T 17966-2024 Information technology microprocessor system floating point operation
3 Terms and definitions
The terms and definitions defined in GB/T 41867-2022 and the following apply to this document.
3.1
[application programming] interface
< artificial intelligence> Syntax and semantic definitions for using artificial intelligence accelerator functions.
[Source. ISO /IEC /IEEE9945.2009, 3.19, modified]
3.2
artificial intelligence accelerating processor
artificial intelligence accelerating chipartificialinteligenceacceleratingchip
An integrated circuit component with a computing microarchitecture that is adapted to artificial intelligence algorithms and can complete the computing and processing of artificial intelligence applications.
Note. In this document, AI accelerator is referred to as accelerator in a context that does not cause misunderstanding.
[Source. GB/T 41867-2022, 3.1.5]
3.3
computationalgraph
A directed graph consisting of nodes and links used to represent mathematical functions.
Note 1.A node represents a mathematical operation, i.e. an operator.
Note 2.Connections represent dependencies between mathematical operations.
Note 3.A connection connects the start node and the end node.
[Source. ISO /IEC /IEEE24765.2017, 3.1762.1, modified]
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