Nnef tagged news

In this EE Times Europe article, Neil Trevett describes how the need for graphics and compute acceleration in embedded markets is growing. Cameras and sensor arrays are increasingly central to many use cases in diverse industries, ranging from automotive to industrial, and are generating increasingly rich data streams that require sophisticated processing. At the same time, advanced user interfaces are being developed using high-quality 3D graphics and even augmented-reality technology. However, the need to deploy accelerated processing, combined with the complexities of safety-critical certification, has created a confusing landscape of processors, accelerators, compilers, APIs, and libraries. That has driven up integration costs for embedded accelerators, which in turn has constrained innovation and time-to-market efficiencies.

Open standards have an important role in helping hardware and software vendors navigate this complex technology environment. Acceleration standards for the embedded market can enable cross-platform software reusability, decouple software and hardware development for easier deployment and integration of new components, provide cross-generation reusability, and facilitate field upgradability. Such standards reduce costs, shorten time to market, and lower the barriers to using advanced techniques such as inferencing and vision acceleration in compelling real-world products.

Join us to help drive the evolution of Machine Learning acceleration standards. ML developers lament the growing fragmentation in the ML ecosystem. Khronos knows that open and royalty-free standards can play an essential role in reducing fragmentation, reducing costs, and providing the industry participants the opportunity to grow. Based on feedback from previous summit and discussions, Khronos is creating a coalition of interested parties to meet the needs of the ML community for hardware acceleration.

Several neural network frameworks for deep learning exist, all of which offer distance features and functionality. Transferring neural networks between frameworks, however, creates extra time and work for developers. The Khronos Group has developed NNEF (Neural Network Exchange Format), an open, royalty-free standard that allows hardware manufacturers to reliably exchange trained neural networks between training frameworks and inference engines. Learn more about NNEF on the Vision Systems Design blog.

The Khronos Group announces a significant expansion in the ecosystem for the NNEF (Neural Network Exchange Format) open, royalty-free standard that enables hardware manufacturers to reliably exchange trained neural networks between training frameworks and inference engines. New and improved NNEF open source convertors, including for TensorFlow Lite and ONNX, enables NNEF to be used to carry trained frameworks from a wider range of training frameworks. A set of extensions to the NNEF 1.0 specification enables NNEF files to contain richer networks of operations and topologies. Finally, an openly available NNEF Model Zoo enables inferencing engines to test their reliable import of NNEF models. More information on NNEF can be found at the Home Page.

This year at Hot Chips, the Khronos Group will have a demo table at which Khronos member AMD will demonstrate using OpenVX for inference using an NNEF model from the model zoo.

All of the presentations and videos from the Khronos OpenVX workshop at the 2019 Embedded Vision Summit are now online. If you were unable to attend this workshop, you may now watch the seven sessions online and follow along with the slide presentations:

• Introduction and OpenCL Overview & Update – Neil Trevett, NVIDIA: slides, video
• OpenCL & SYCL – Andrew Richards, Codeplay: slides, video
• Intel Open Source SYCL Compiler Project – Konstantin S. Bobrovsky, Intel: slides, video
• OpenVX Presentations – Frank Brill, Cadence / Niclas Danielsson & Mikael Pendse, Axis : here & here, video
• Inference with OpenVX – Mike Schmit, AMD: slides, video
• NNEF Presentation – Gergely Debreczeni, AImotive: slides, video
• OpenVX Hands-On - Part 1 – Rajy Rawther & Kiriti Nagesh Gowda, AMD: slides, video

Khronos member Au-Zone Technologies has written a guest post on the CNX Embedded Software blog showing how to add Machine Learning (ML) processing to an embedded product with the help from the Khronos Groups Neural Network Exchange Format (NNEF). The post illustrates, with an example implementation, how to detect and classify different pasta types on a moving conveyor belt.

The Khronos Group was in Japan this week for SIGGRAPH Asia 2018. There were five BOF sessions covering Vulkan, OpenXR, WebGL, glTF, NNEF, OpenVX and OpenCL. Most of the presentations from these sessions is now online and we have lots of photos as well. Unfortunately not video this year.

To further its goal of passing trained frameworks to embedded inference engines, the Khronos Group adds to its existing converters with two new bidirectional converters. Now available on the NNEF GitHub, these new tools enable easy conversion of trained models, including quantized models, between TensorFlow or Caffe2 formats and NNEF format.

AImotive, the global provider of full stack, vision-first self-driving technology, today announced the release of aiWare3, the company’s 3rd generation, scalable, low-power, hardware Neural Network (NN) acceleration core. The scalable aiWare3 architecture facilitates low-power continuous operation for autonomous vehicles (AVs) with up to 12 or more high-resolution cameras, LiDARs and/or radar. aiWare3 delivers up to 50 TMAC/s (> 100 TOPS) per chip at more than 2 TMAC/s (4 TOPS) per W1. aiWare3’s IP core is supported by a comprehensive software development kit (SDK) that uses The Khronos Group’s NNEF standard. It will ship to lead customers in Q1 2019.

AImotive’s aiWare3 Hardware IP Helps Drive Autonomous Vehicles Towards Production.

Neil Trevett, President of the Khronos Group, delivers the presentation “Update on Khronos Standards for Vision and Machine Learning” at the Embedded Vision Alliance’s September 2018 Vision Industry and Technology Forum. Neil Trevett shares updates on recent, current and planned Khronos standardization activities aimed at streamlining the deployment of embedded vision and AI. For the full version of this video, along with hundreds of others on various embedded vision topics, please visit the Embedded Vision website.

Khronos member Peter McGuinness has written an overview about NNEF over on the GFXSpeak blog. The new standard was released in provisional form in December of 2017 and, after a period of consultation with industry, is now ratified in its final form and available for immediate use. As well as the standard itself, Khronos is simultaneously releasing a suite of open source tools to allow developers to immediately begin using the format with the three most popular training frameworks: Tensorflow and Caffe/Caffe2. All of these tools are available on GitHub in the Khronos repo. Learn more about NNEF.

The Khronos Group announces updates to key standards and opens the Khronos Education Forum at SIGGRAPH. With various Khronos events throughout the week, including a day of Birds of a Feather (BOF) sessions and its annual networking reception, Khronos is accelerating open standards ecosystems and continuing its commitment to the SIGGRAPH community of interactive graphics professionals. At SIGGRAPH, Khronos will be talking about the following standards developments and initiatives: NNEF 1.0 Specification Finalized, OpenXR Demonstrates Specification in Hardware Implementation, Ecosystem Grows; New Extensions Released and a Call for Participation - Education Forum Opens for Public Contribution. In addition to standards updates, The Khronos Group is hosting educational sessions and networking events this week, including a full-day of BOF sessions with talks from various members.