IEEE P2941.2/D3, Jul 2023
IEEE Draft Standard for Application Programming Interface (API) of Deep Learning Inference Engine
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- Language: English
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About This Item
IEEE P2941.2/D3, Jul 2023 is a draft standard for the application programming interface (API) of a deep learning inference engine, aimed at the computing and processing field. It is relevant to software teams working on inference-layer interoperability, where consistent API behavior can affect model deployment, integration, and testing. As a draft document, IEEE P2941.2/D3, Jul 2023 helps define a technical framework that may support more predictable implementation across deep learning systems and related tools.
About IEEE P2941.2/D3, Jul 2023
This draft focuses on the interface layer used to invoke and manage deep learning inference functions, rather than the model itself. In practical terms, it is intended to guide how an inference engine may be accessed, configured, and controlled through an API in computing environments. IEEE P2941.2/D3, Jul 2023 is useful for developers, integrators, and test teams that need a common technical reference when comparing implementations or validating compatibility in processing workflows.
Where is IEEE P2941.2/D3, Jul 2023 used?
IEEE P2941.2/D3, Jul 2023 is most relevant in software and hardware systems that run deep learning inference as part of a larger computing pipeline. That can include embedded platforms, edge devices, server-based AI services, and application frameworks that rely on a defined API to submit inputs, retrieve outputs, or manage runtime behavior. It may also be used during toolchain integration, conformance testing, and procurement of inference software components where interface clarity matters.
Importance in practice
For organizations building or evaluating inference software, this draft standard can help reduce ambiguity around API behavior and implementation expectations. Clear interface guidance supports more consistent testing, better design control, and easier comparison between products or versions. IEEE P2941.2/D3, Jul 2023 may also assist teams working to limit integration risk, especially when deep learning inference must operate reliably across different computing platforms, deployment targets, or software stacks.
- Deep learning inference API structure
- Implementation and integration reference
- Testing and compatibility support
- Computing and processing use cases
- Publication Date: 2023
- Standard Status: Inactive
- Publisher: IEEE
- Subject: Computing and Processing
- Official IEEE: Doi link
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