NNEF Overview

Neural Network Exchange Format (NNEF)

NNEF reduces 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.

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. A stable, flexible and extensible standard that equipment manufacturers can rely on is critical for the widespread deployment of neural networks onto edge devices, and so 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.

NNEF - Solving Neural Net Fragmentation

Convolutional Neural Networks (CNN) are computationally expensive, and so many companies are actively developing mobile and embedded processor architectures to accelerate neural net-based inferencing at high speed and low power. As a result of such rapid progress, the market for embedded neural net processing is in danger of fragmenting, creating barriers for developers seeking to configure and accelerate inferencing engines across multiple platforms.

Today, most neural net toolkits and inference engines use proprietary formats to describe the trained network parameters, making it necessary to construct many proprietary importers and exporters to enable a trained network to be executed across multiple inference engines.

NNEF has been designed to be reliably exported and imported across tools and engines such as Torch, Caffe, TensorFlow, Theano, Chainer, Caffe2, PyTorch, and MXNet. The NNEF 1.0 Specification covers a wide range of use-cases and network types with a rich set of operations and a scalable design that borrows syntactical elements from existing languages with formal elements to aid in correctness. NNEF includes the definition of custom compound operations that offers opportunities for sophisticated network optimizations. Future work will build on this architecture in a predictable way so that NNEF tracks the rapidly moving field of machine learning while providing a stable platform for deployment.

Embedded Vision and Inferencing Acceleration

NNEF 1.0

Released as a stable version after getting industry feedback based on provisional version

• Welcoming further comments and feedback on Khronos GitHub

Initial focus on passing trained frameworks to embedded inference engines

• Authoring interchange, importing NNEF into tools, is also an emerging use case

Support deployable range of network topologies

• Rapid evolution to encompass new network types as they emerge from research

NNEF File Structure

Split Structure and Data files

• Easy independent access to network structure or individual parameter data
• Set of files can use a container such as tar or zip with optional compression and encryption

NNEF Tools Ecosystem

NNEF open source projects hosted on Khronos
NNEF GitHub repository under Apache 2.0

NNEF Implementations and Roadmap

Active NNEF roadmap development

• Track development of new network types
• Define conformance testing procedure
• Address an ever wider range of applications
• Increase the expressive power of the format

Participation

NNEF Working Group Participants

NNEF welcomes the participation of the machine learning community to make NNEF useful for their own workflows. In addition, NNEF is working closely with the Khronos OpenVX™ working group to enable ingestion of NNEF files. The OpenVX Neural Network extension enables OpenVX 1.2 to act as a cross-platform inference engine, combining computer vision and deep learning operations in a single graph.

NNEF and ONNX

Embedded Inferencing Import	Training Interchange
Defined Specification	Open Source Project
Multi-company Governance at Khronos	Initiated by Facebook & Microsoft
Stability for hardware deployment	Software stack flexibility