Neural Network Exchange Format


Neural Network Exchange Format is an artificial neural network data exchange format developed by the Khronos Group. It is intended to reduce machine learning deployment fragmentation by enabling a rich mix of neural network training tools and inference engines 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 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 the and tools are available on

Versions

The following Khronos members have participated in the NNEF working group:

Tools

The on GitHub contains the following open source tools: