Neural Network Exchange Format (NNEF) is an
artificial neural network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected unit ...
data exchange format
Data exchange is the process of taking data structured under a ''source'' schema and transforming it into a ''target'' schema, so that the target data is an accurate representation of the source data.A. Doan, A. Halevy, and Z. Ives.Principles of da ...
developed by the
Khronos Group
The Khronos Group, Inc. is an open, non-profit, member-driven consortium of 170 organizations developing, publishing and maintaining royalty-free interoperability standards for 3D graphics, virtual reality, augmented reality, parallel computation ...
. It is intended to reduce
machine learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.
Machine ...
deployment fragmentation by enabling a rich mix of neural network training tools and
inference engine
In the field of artificial intelligence, an inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert ...
s to be used by applications across a diverse range of devices and platforms.
History
NNEF was proposed in 2015 by member companies of the Khronos Group as a device and implementation independent transfer format capable of describing any artificial neural net in terms of its structure, operations and data.
The first version of the standard was launched in provisional form in December 2017, and was ratified as an official Khronos standard in August 2018.
Objectives
The goal of NNEF is to enable data scientists and engineers to easily transfer trained networks from their chosen training framework into a wide variety of inference engines. NNEF encapsulates a complete description of the structure, operations and parameters of a trained neural network, independent of the training tools used to produce it and the inference engine used to execute it.
Governance and Availability
NNEF is maintained by the Khronos Group under its Open Governance Principles
[Khronos IP Framework](_blank)
/ref> as follows:
* Any company is invited and able to join Khronos to contribute to and influence the development of its specifications;
* Finalized specifications are publicly and freely distributed at zero cost from the Khronos web-site;
* Any company can implement a Khronos specification and participating implementers can obtain a trademark license for conformant implementations and pay zero royalties to Khronos participants; and
* Developers may freely use implementations of Khronos specifications.
The NNEF specification is available on th
Khronos NNEF registry
and tools are available o
Github
Versions
* NNEF 1.0 Provisional, Released 20 December 2017.[v1.0p Khronos PR](_blank)
/ref>
* NNEF 1.0, Released 13 August 2018
Industry Participation
The following Khronos members have participated in the NNEF working group:
Tools
Th
NNEF tools project
on GitHub contains the following open source tools:
* File format Parser
* Bidirectional converters between NNEF and ONNX, Caffe, Caffe2, TensorFlow (python), TensorFlow (protobuf)
*Model zoo: reference collection of models converted to NNEF
See also
* Open Neural Network Exchange
The Open Neural Network Exchange (ONNX) [] is an Open-source software, open-source artificial intelligence ecosystem of technology companies and research organizations that establish open standards for representing machine learning algorithms and ...
References
{{Khronos Group standards
Neural network data exchange formats