Xilinx, Inc announced expansion into a wide range of vision guided machine learning applications with the Xilinx reVISION stack. Developers with limited hardware expertise can use a C/C++/OpenCL development flow with industry-standard frameworks and libraries like Caffe and OpenCV to develop embedded vision applications on a single Zynq SoC or MPSoC. For application level development, Xilinx supports industry-standard frameworks including Caffe for machine learning and OpenVX for computer vision.
Furian is designed to address the increasing compute requirements across multiple applications and market segments with efficient use of compute APIs including OpenCL 2.0, Vulkan 1.0 and OpenVX 1.1*. Furian adds a bi-directional GPU/CPU coherent interface for efficient sharing of data; and a transition to user mode queues from kernel mode queues which reduces latency and CPU utilization for compute operations. Based on a published Khronos specification, GPUs based on the PowerVR Furian architecture are expected to pass the Khronos Conformance Testing Process. Current conformance status can be found at www.khronos.org/conformance.
This project is an OpenCL-based simulator for brain models built using Nengo. It can be orders of magnitude faster than the reference simulator in nengo for large models. Nengo is a Python library for creating and simulating large-scale brain models.
NVIDIA graphics driver for Windows version 378.66 is now offering some OpenCL 2.0 support. From the release notes: "New features in OpenCL 2.0 are available in the driver for evaluation purposes only." Some known issues include: The current implementation is limited to 64-bit platforms only; OpenCL 2.0 allows kernels to be enqueued with global_work_size larger than the compute capability of the NVIDIA GPU. The current implementation supports only combinations of global_work_size and local_work_size that are within the compute capability of the NVIDIA GPU; For executing kernels (whether from the host or the device), OpenCL 2.0 supports non-uniform ND-ranges where global_work_size does not need to be divisible by the local_work_size. This capability is not yet supported in the NVIDIA driver, and therefore not supported for device side kernel enqueues.
Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). The test took place in Nanjing City, China, where ZTE’s engineers used Intel’s midrange Arria 10 FPGA for a cloud inferencing application using a CNN algorithm. The benchmark was achieved on a server holding 4S Intel Xeon E5-2670v3 processors running at 2.30GHz, 128GB DDR4; Intel PSG Arria 10 FPGA Development Kit with one 10AGX115 FPGA, 4GB DDR4 SODIMM, Intel Quartus Prime and OpenCL SDK v16.1. Besides the impressive increase in performance, the team at the ZTE Wireless Institute sped design time with the use of the OpenCL programming language.
FotoNation Limited and VeriSilicon Holdings Co., Ltd have entered into an agreement to jointly develop a next generation image processing platform that offers best-in-class programmability, power, performance and area for computer vision (CV), computational imaging (CI) and deep learning. The market-ready IP platform, named IPU 2.0, will be available for customer license and design in the first quarter of 2017. IPU 2.0 offers a unified programing environment and pre-integrated imaging features for a wide range of applications across surveillance, automotive, mobile, IoT and more. IPU 2.0 will use open initiatives such as OpenVX and OpenCL.
Khronos made videos of three presentations from Codeplay at the Khronos Booth. The videos cover "Heterogeneous C++ dispatch: Comparing SYCL to HPX, KoKKos, & Raja, "Khronos SYCL Parallel STL Open-source Project" and "Getting Your Hands on SYCL Using the ComputeCpp Community Edition"
Khronos issued a Request For Quote (RFQ) back in September 2016 to enhance and expand the existing OpenCL 2.1 conformance tests to create an OpenCL 2.2 test suite to be used to define conformance for OpenCL 2.2 implementations. The contract has been awarded to StreamComputing. StreamComputing is a software consultancy company specialized in performance tuned software development for CPU, GPU and FPGA. A large part of their clients hires them for their OpenCL expertise.