Kiriti Nagesh Gowda from the AMD Radeon Technology Group will be speaking at Graphicon 2019 in Bryansk, Russia on September 25th 2019. The talk will be about the MIVisionX toolkit and how to run inference efficiently using OpenVX and OpenVX Extensions.
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.
Frank Brill, Design Engineering Director at Cadence, presents the “Portable Performance via the OpenVX Computer Vision Library: Case Studies” tutorial at the May 2019 Embedded Vision Summit. For the full version of this video, along with hundreds of others on various embedded vision topics, please visit the Embedded Vision website.
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:
Cadence Design Systems and ArcSoft announced they have partnered to develop AI and vision applications for Cadence Tensilica Vision DSPs. ArcSoft has collaborated with Cadence to port beauty shot, high dynamic range (HDR), bokeh and facial unlock applications to the Vision P6 DSP. The software environment includes complete, optimized support for more than 1,500 OpenCV-based vision and OpenVX 1.1 library functions.
The Khronos Group is accepting proposals for an OpenVX project. The project will deliver a fully conformant implementation of the OpenVX 1.2.1 standard that is optimized for the Raspberry Pi 3 Model B+ (or similar) platform. The project will demonstrate the performance advantage of using the OpenVX API by implementing several optimizations that are enabled by OpenVX. Deadline for submissions is January 15, 2019. Complete details here.
The Khronos Group is accepting proposals for an OpenVX project. The project will deliver a fully conformant implementation of the OpenVX 1.2.1 standard that is optimized for the Raspberry Pi 3 Model B+ (or similar) platform. The project will demonstrate the performance advantage of using the OpenVX API by implementing several optimizations that are enabled by OpenVX. Deadline for submissions is January 15, 2019. Complete details here.
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.
Percepio Tracealyzer for OpenVX allows you to visualize the execution of OpenVX applications and identify bottlenecks where optimization can make a big difference. Tracealyzer for OpenVX is initially available for Synopsys EV6x embedded vision processors, leveraging the built-in trace support in Synopsys ARC MetaWare EV Development Toolkit. Percepio Application Note PA-025 describes how to get started with Tracealyzer for OpenVX, using Synopsys EV6x processors and Synopsys MetaWare EV Development Toolkit.
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.
NXP delivers a wide range of processing solutions on which machine-learning (ML) applications can run. Developers will need the associated software and tools to make them work and this is where eIQ framework and development tools come into play. The eIQ framework is designed to work with hardware abstraction layers like OpenCL, OpenVX, and the Arm Compute Library, as well as inference engines like the Arm NN (neural net), Android NN, GLOW, and OpenCV.
OpenVINO is a comprehensive toolkit for developing applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends CV workloads across Intel hardware, maximizing performance. OpenVINO enables CNN-based deep learning inference on the edge; supports heterogeneous execution across computer vision accelerators—CPU, GPU, Intel Movidius Neural Compute Stick, and FPGA—using a common API; and includes optimized calls for OpenCV and OpenVX.
The Khronos Group announces the ratification and the public release of the NNEF™ 1.0 (Neural Network Exchange Format) specification. After gathering feedback from the industry review of the provisional specification, Khronos releases NNEF 1.0 as a stable, flexible, and extensible open standard for hardware manufacturers to reliably deploy optimized, accelerated neural network inferencing onto diverse edge devices. Together with this release, an ecosystem of tools is now also available on GitHub, including an NNEF parser and converters from Tensorflow and Caffe. Importers into popular inferencing environments, including Android’s Neural Network API (NNAPI) and Khronos’ OpenVX™, are also being developed.
The Embedded Vision Alliance has posted a follow-on article showcasing several case study examples of OpenVX implementations in various applications, leveraging multiple hardware platforms along with both traditional and deep learning computer vision algorithms. The article also introduces readers to an industry alliance created to help product creators incorporate practical computer vision capabilities into their hardware and software, along with outlining the technical resources that this alliance provides. A companion article focuses on more recent updates to the OpenVX API, up to and including latest v1.2 of the specification and associated conformance tests, along with the recently published set of extensions that OpenVX implementers can optionally provide. It also discusses the optimization opportunities available with SoCs’ increasingly common heterogeneous computing architectures.