If you’re tired of all the noise around the U.S. election the initials CNN are probably not what you want to be seeing right now. However, in our upcoming webinar CNNs are exactly what we’ll be talking about. However, in this case, by CNN we are referring to
Tag: GPU Compute
It was clear last week at the annual Embedded Vision Summit in Santa Clara that the time of computer vision and deep learning on mobile had finally arrived. Interest in the area is growing noticeably – the Summit program expanded from one to two days this year, there
Vulkan™ started more than a year ago as a cross-industry effort to develop an explicit API designed to work optimally on today’s leading-edge hardware. The activity has been managed by the Khronos™ Group, the same non-profit organization that develops popular APIs such as OpenGL ES, OpenCL, WebGL, or
Imagination designed its PowerVR Tile-Based Deferred Rendering (TBDR) graphics architecture more than 20 years ago with a focus on efficiency across performance, power consumption and system level integration. This approach has equally been applied to our integration of compute functionality in our GPU architecture; PowerVR Rogue is the
After exploring a quick guide to writing OpenCL kernels for PowerVR Rogue GPUs and analyzing a heterogeneous compute case study focused on image convolution filtering, I am going to spend some time looking at how developers can measure the performance of their OpenCL kernels on PowerVR Rogue GPUs.
In a previous article we described our PowerVR Imaging Framework, a set of extensions to the OpenCL and EGL APIs that enable efficient zero-copy sharing of memory between a PowerVR GPU and other system components such as a CPU, ISP and VDE. Most flows use EGL to facilitate
James Price is currently completing a PhD degree at the Department of Computer Science, University of Bristol. When developing programs that utilise GPU compute via OpenCL, we can’t use our traditional CPU development tools. This can make debugging complex OpenCL kernels challenging. As part of my PhD, funded
Writing and optimizing code for heterogeneous computing can be difficult, especially if you are starting from scratch. Imagination has set up a new page where developers can access the source code for an example camera and video post-processing application that leverages the PowerVR Imaging Framework to implement efficient
Imagination’s R&D group has developed a face detection algorithm, which is based on a classifier cascade and is optimized to run on mobile devices comprising a CPU and PowerVR GPU. The algorithm employs several optimizations to improve performance and accuracy. In particular, instead of searching each entire frame
Computer vision is the use of computers to extract useful meaning from images, such as those that arise from photographs, video and real-time camera feeds. Thanks to the proliferation of low-power parallel processors, the increasing availability of 3D sensors and an active ecosystem of algorithm developers, it is
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