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Khronos Group Releases v1.0 Provisional Specification for the Neural Network Exchange Format

Accepted submission by takyon at 2017-12-21 03:53:27
Software

Khronos Group has released a specification [khronos.org] for a format intended to allow the transfer of trained neural networks between different frameworks and hardware [anandtech.com]. It is expected to be finalized within 3-6 months:

Today the Khronos Group, the industry consortium behind OpenGL and Vulkan, released a v1.0 provisional specification for its Neural Network Exchange Format (NNEF). First announced last year, this provisional stage is aimed at getting industry feedback on real-world use. While its name encapsulates its purpose, more specifically NNEF will act as a compatible format that can transfer trained neural networks between frameworks or to a wide range of inferencing hardware. Khronos is hoping that NNEF will act as a common format for all the myriad frameworks, such as Caffe, TensorFlow, Theano, and Torch, and be as ubiquitous in neural network porting in the same way PDFs are used for documents.

Much of the strength of NNEF comes from its bifurcated file structure, where there is a general and compatible flat level along with a complicated and optimizable compositional level. NNEF has also been designed with the understanding that deep learning is still a young and rapidly advancing field, where certain AI or neural network methods or framework types may become quickly displaced.

NNEF will also complement Khronos' OpenVX, a high-level graph-based API intended for cross-platform use in computer vision, and both working groups have already been collaborating. The upcoming releases of OpenVX will feature a NNEF Import extension, which would provide more flexibility to the format. As a specification, NNEF does not include tools, and Khronos is pursuing an open source strategy, with current projects on an NNEF syntax parser/validator and exporters for specific frameworks.

Khronos site [khronos.org] and press release [khronos.org]. Github [github.com].

EE Times article from May [eetimes.com].


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