Tag: GPU Compute


UPDATE: ELVEES’ new ELISE SoC targeting ADAS, smart devices, IoT, augmented reality and other computer vision applications has been announced in a press release earlier this week. You can read more about the partnership between Imagination and ELVEES on our website. If you follow this blog closely (and I hope you do), you may have read several articles recently about

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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 OpenVX as well as the universal file format initiatives like COLLADA. Imagination

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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 most recent version of our GPU architecture and it fully supports mobile

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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. The performance of scalar code running on a CPU depends upon how

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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 by Imagination Technologies, I’ve developed an OpenCL device simulator called Oclgrind, which

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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 zero-copy flows for a range of image processing kernels. To download the

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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 for faces, the detector limits its search to regions in which faces

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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 now possible for many embedded devices to analyse their environments on-demand or

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