IEEE 2941.1-2022

$105.63

IEEE Standard for Operator Interfaces of Artificial Intelligence (Approved Draft)

Published By	Publication Date	Number of Pages
IEEE	2022	328

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Description

New IEEE Standard – Active. A set of operator interfaces frequently used in artificial intelligence (AI) applications is defined in this standard, where the AI operators refer to the standard building blocks and primitives for performing basic AI operations. The functionality and the specific input and output operands of an AI operator are discussed, as well as both generality and efficiency. Various types of operators, such as those related to basic mathematics, neural network, and machine learning, are highlighted.

PDF Catalog

PDF Pages	PDF Title
1	IEEE Std 2941.1™-2022 Front Cover
2	Title page
4	Important Notices and Disclaimers Concerning IEEE Standards Documents
8	Participants
10	Introduction
11	Contents
13	1. Overview 1.1 Scope 1.2 Purpose
14	1.3 Word usage 2. Normative references 3. Definitions, acronyms, and abbreviations 3.1 Definitions
15	3.2 Acronyms and abbreviations
16	4. Symbols and operators 4.1 Arithmetic operators
17	4.2 Logical operators 4.3 Relational operators 4.4 Bitwise operators
18	5. General principles 5.1 Starting subscript 5.2 Order of parameters 5.3 Programming language 5.4 Broadcasting 5.5 Error handling 5.6 Interface consistency between dense and sparse tensors
19	5.7 Functional consistency and hierarchical difference 6. Data structure 6.1 Element data type 6.2 Shape information 6.3 Layout information
20	6.4 Device information 6.5 Other extensions 7. Interfaces for basic mathematical operators 7.1 Tensor creation and destruction
30	7.2 Query and inspection
33	7.3 Tensor conversion
44	7.4 Arithmetic operations
50	7.5 Comparison operation
54	7.6 Logical operation
56	7.7 Bitwise operation
59	7.8 Power function
61	7.9 Rounding operation
62	7.10 Trigonometric function
65	7.11 Hyperbolic function
68	7.12 Exponential and logarithmic function
71	7.13 Reduction
72	7.14 Indexing
75	7.15 Complex
77	7.16 Signal processing
78	7.17 Linear algebra
84	8. Interfaces for neural network operators 8.1 Activation function
101	8.2 Loss function
111	8.3 Regularization function
113	8.4 Normalization function
121	8.5 Pooling function
127	8.6 Convolution function
138	8.7 Evaluation function
139	8.8 Recurrent network function
155	8.9 Encoding function
157	8.10 Distance function 8.11 Visual function
159	8.12 Optimizer function
167	9. Interfaces for machine learning operators 9.1 Linear regression algorithm
169	9.2 Logistic regression algorithm
171	9.3 Decision tree classifier (DTC)
173	9.4 Decision tree regressor (DTR)
175	9.5 Random forest classifier (RFC)
177	9.6 Random forest regressor (RFR)
179	9.7 Gaussian naïve Bayesian algorithm (GNB)
181	9.8 Linear discriminant analysis (LDA)
183	9.9 Principal component analysis (PCA)
185	9.10 K-nearest neighbor algorithm (KNN)
188	9.11 Support vector machine (SVM) algorithm
192	9.12 K-means clustering (Kmeans)
195	Annex A (informative) C reference of operator interfaces A.1 Data structure
197	A.2 C interfaces for basic mathematical operators
246	A.3 C interfaces for neural network operators
304	A.4 C interfaces for machine learning operators
327	Annex B (informative) Bibliography
328	Back Cover