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“Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques,” a Presentation from Imagination Technologies

Paul Brasnett, Senior Research Manager for Vision and AI in the PowerVR Division at Imagination Technologies, presents the “Improving and Implementing Traditional Computer Vision Algorithms Using DNN Techniques” tutorial at the May 2018 Embedded Vision Summit. There has been a very significant shift in the computer vision industry over the past few years, from traditional …

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“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit. Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the …

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies Read More +

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit. Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the …

“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies Read More +

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Imagination’s Smart, Efficient Approach to Mobile Compute

This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. 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 …

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Measuring GPU Compute Performance

This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. 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 …

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Supported Zero-copy Flows Inside the PowerVR Imaging Framework

This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. 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 …

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The PowerVR Imaging Framework Camera Demo

This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. 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 …

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Deep Dive: Implementing Computer Vision with PowerVR (Part 3: OpenCL Face Detection)

This article was originally published at Imagination Technologies' website, where it is one of a series of articles. It is reprinted here with the permission of Imagination Technologies. 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 …

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“Efficient Convolutional Neural Network Inference on Mobile GPUs,” a Presentation from Imagination Technologies

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Efficient Convolutional Neural Network Inference on Mobile GPUs" tutorial at the May 2016 Embedded Vision Summit. GPUs have become established as a key tool for training of deep learning algorithms. Deploying those algorithms on end devices is a key enabler to their commercial success and …

“Efficient Convolutional Neural Network Inference on Mobile GPUs,” a Presentation from Imagination Technologies Read More +

Optimizing Computer Vision Applications Using OpenCL and GPUs

The substantial parallel processing resources available in modern graphics processors makes them a natural choice for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across GPUs and other heterogeneous processing elements within a system. In this article, we briefly review parallelism in computer vision applications, …

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