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Tag: Heterogeneous Compute
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
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
Modern mobile application processors are highly heterogeneous, combing a variety of different hardware components optimized for different tasks. As shown in the figure below, a processor designed for vision might include an Image Signal Processor (ISP) for acquiring image sensor data, a vector processor such as a GPU
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
In a previously published article, I offered a quick guide to writing OpenCL kernels for PowerVR Rogue GPUs; this sets the scene for what follows next: a practical case study that analyzes image convolution kernels written using OpenCL. Many image processing tasks such as blurring, sharpening and edge
In my previous article about heterogeneous architectures, I identified memory bandwidth as the main bottleneck for implementing power-efficient algorithms for computer vision. Luckily, Imagination has created an innovative solution designed to address this common issue in mobile and embedded devices – and it comes in the form of
Heterogeneous architectures in embedded computing are fast becoming a reality – we indeed see many leading IP and semiconductor companies today building heterogeneous computing hardware. In the article below, I’m going to describe one typical use case for heterogeneous computing and the challenges that result from moving to
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