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Figure5

Combining an ISP and Vision Processor to Implement Computer Vision

Algorithms, Articles, Imagination Technologies, Processors, Sensors, Software, Synopsys, Technical Articles, Tools /

An ISP (image signal processor) in combination with one or several vision processors can collaboratively deliver more robust computer vision processing capabilities than vision processing is capable of providing standalone. However, an ISP operating in a computer vision-optimized configuration may differ from one functioning under the historical assumption that its outputs would be intended for …

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

Algorithms, Imagination Technologies, Processors, Software, Summit 2018, Tools, Videos /

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

Algorithms, Imagination Technologies, Processors, Software, Tools, Videos /

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 …

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

Algorithms, Imagination Technologies, Processors, Software, Tools, Videos /

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 …

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Imagination’s Smart, Efficient Approach to Mobile Compute

Articles, Imagination Technologies, Technical Articles /

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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20-Parallel-versus-serial-execution-of-a-statement-in-a-warp

Measuring GPU Compute Performance

Articles, Imagination Technologies, Technical Articles /

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

Articles, Imagination Technologies, Technical Articles /

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

Articles, Imagination Technologies, Technical Articles /

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)

Articles, Imagination Technologies, Technical Articles /

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

Algorithms, Imagination Technologies, Processors, Software, Tools, Videos /

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 …

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