Superseded
Draft standard
Historical
IEEE P3301
IEEE Draft Standard - Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Artificial Intelligence Framework (AIF) Version 1.1
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Summary
New IEEE Standard - Superseded - Draft.
The MPAI AI Framework (MPAI-AIF) Technical Specification specifies architecture, interfaces, protocols and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for execution of AI-based implementations, but also suitable for mixed AI and traditional data processing workflows.
MPAI-AIF possesses the following main features: • Operating System-independent. • Component-based modular architecture with standard interfaces. • Interfaces encapsulate Components to abstract them from the development environment. • Interface with the MPAI Store enables access to validated Components. • Component can be implemented as: software only (from Micro-Controller Units to High-Performance Computing), hardware only, and hybrid hardware-software. • Component system features are: • Execution in local and distributed Zero-Trust architectures. • Possibility to interact with other Implementations operating in proximity. • Direct support to Machine Learning functionalities.
The MPAI AI Framework (MPAI-AIF) Technical Specification specifies architecture, interfaces, protocols and Application Programming Interfaces (API) of an AI Framework (AIF), especially designed for execution of AI-based implementations, but also suitable for mixed AI and traditional data processing workflows.
MPAI-AIF possesses the following main features: • Operating System-independent. • Component-based modular architecture with standard interfaces. • Interfaces encapsulate Components to abstract them from the development environment. • Interface with the MPAI Store enables access to validated Components. • Component can be implemented as: software only (from Micro-Controller Units to High-Performance Computing), hardware only, and hybrid hardware-software. • Component system features are: • Execution in local and distributed Zero-Trust architectures. • Possibility to interact with other Implementations operating in proximity. • Direct support to Machine Learning functionalities.
Notes
Superseded
Technical characteristics
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Publication Date | 07/29/2022 |
| Edition | |
| Page Count | 66 |
| EAN | --- |
| ISBN | --- |
| Weight (in grams) | --- |
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