Five years ago The International Workshop on OpenCL (IWOCL – “eye-wok-ul”) started as a small OpenCL-focused conference. In 2017 it has grown to three full days filled with tutorials, talks, posters and many technical discussions. You’ll hear attendees (and yourself) saying, “I did not know this was going on and I should have known it before.” It is a great place to learn the latest on OpenCL. Learn more about the history of IWOCL and the upcoming IWOCL event May 16-18, 2017 in Toronto, Canada.

This two part WebGL article (Part 1, Part 2) outlines several techniques for resolving common WebGL performance issues seen with games published on Kongregate.

VeriSilicon Holdings Co., Ltd. announces VIP8000, a highly scalable and programmable processor for computer vision and artificial intelligence. It delivers over 3 Tera MACs per second, with power consumption more efficient than 1.5 GMAC/second/mW and the smallest silicon area in industry with 16FF process technology. The VIP8000 can directly import neural networks generated by popular deep learning frameworks, such as Caffe and TensorFlow and neural networks can be integrated to other computer vision functions using the OpenVX framework. The processor is programmed by OpenCL or OpenVX with a unified programming model across the hardware units, including customer application-specific hardware acceleration units. Learn more about the VIP8000.

This week at the Embedded Vision Summit (EVS) in California Imagination is showcasing their latest Convolutional Neural Network (CNN) object recognition demo. All of these networks have been implemented using Imagination’s own DNN library. IMG DNN sits on top of OpenCL but doesn’t obscure it, and makes use of OpenCL constructs so it can be used alongside other custom OpenCL code. Imagination’s Paul Brasnett is talking at EVS on the subject of ‘Training CNNs for Efficient Inference‘ and for further reading, take a look at this CNN based number recognition demo, which uses OpenVX with CNN extension. Learn more about Imagination’s Convolutional Neural Networks.

The Intel Computer Vision SDK Beta is for developing and deploying vision-oriented solutions on platforms from Intel, including autonomous vehicles, digital surveillance cameras, robotics, and mixed-reality headsets. Based on OpenVX, this SDK offers many useful extensions and supports heterogeneous execution across CPU and SoC accelerators using an advanced graph compiler, optimized and developer-created kernels, and design and analysis tools. It also includes deep-learning tools that unleash inference performance on deep-learning deployment. If the functionality you need is not already available in the supplied library, you can create custom kernels using C, C++, or OpenCL kernels.

The ARM team has updated the Vulkan SDK with new sample code and tutorials. All sample code is released in github, under an MIT license. This latest SDK update includes two new Vulkan features, Vulkan Multipass and Adaptative Scalable Texture Compression, with ARM Mali sample code and tutorials.

IWOCL–The International Workshop on OpenCL is happening in just two weeks in Toronto Canada on May 16th. The complete program is now online and there is still room for more folks. Learn more about IWOCL. The program includes 4 tutorials, DHPC++ 2017, 19 technical sessions, the Khronos panel, posters and the conference dinner and networking event.

The new Samsung OS–Tizen 3.0–will support Vulkan. The Z4, Samsung’s newest phone will be running Tizen 3.0 and supporting Vulkan.

The Khronos Group announces the immediate release of the OpenVX 1.2 specification for cross-platform acceleration of computer vision applications and libraries. OpenVX is a high-level, graph-based API targeted at real-time mobile and embedded platforms. This open, cross-platform, royalty-free standard enables performance-portable, power-optimized computer vision applications such as face, body, and gesture tracking, smart video surveillance, autonomous driver assistance systems, visual inspection, and robotics. Core OpenVX 1.2 has significantly expanded functionality, including conditional execution, feature detection, and classification operations.