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“Deep Learning from a Mobile Perspective,” a Presentation from Caffe Developer Yangqing Jia

Yangqing Jia created the Caffe framework while a graduate student researcher at UC Berkeley. He later was a member of the Google Brain project and recently joined Facebook, working on various aspects of deep learning research and engineering. At the Alliance’s February 2016 tutorial on deep learning for computer vision using convolutional neural networks and …

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Optimizing Fast Fourier Transformation on ARM Mali GPUs

This article was originally published at ARM's website. It is reprinted here with the permission of ARM. The Fast Fourier Transformation (FFT) is a powerful tool in signal and image processing. One very valuable optimization technique for this type of algorithm is vectorization. This article discusses the motivation, vectorization techniques and performance of the FFT …

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Speeding Up the Fast Fourier Transform Mixed-Radix on Mobile ARM Mali GPUs By Means of OpenCL (Part 3)

This article was originally published at ARM's website. It is reprinted here with the permission of ARM. For more information, please see ARM's developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. In this third and last part of this blog series we are going to extend the mixed-radix FFT OpenCL™ …

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Speeding Up the Fast Fourier Transform Mixed-Radix on Mobile ARM Mali GPUs By Means of OpenCL (Part 2)

This article was originally published at ARM’s website. It is reprinted here with the permission of ARM. For more information, please see ARM’s developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. Here we are for the second part of our blog series about the OpenCL™ implementation of Complex to Complex …

Speeding Up the Fast Fourier Transform Mixed-Radix on Mobile ARM Mali GPUs By Means of OpenCL (Part 2) Read More +

Speeding Up the Fast Fourier Transform Mixed-Radix on Mobile ARM Mali GPUs By Means of OpenCL (Part 1)

This article was originally published at ARM's website. It is reprinted here with the permission of ARM. For more information, please see ARM's developer site, which includes a variety of GPU Compute, OpenCL and RenderScript tutorials. This is the first article of three that will focus on the implementation of Fast Fourier Transform (FFT) using …

Speeding Up the Fast Fourier Transform Mixed-Radix on Mobile ARM Mali GPUs By Means of OpenCL (Part 1) Read More +

“An Update on Open Standard APIs for Vision Processing,” a Presentation from Khronos

Neil Trevett, President of Khronos and Vice President at NVIDIA, delivers the presentation, "Update on Khronos Open Standard APIs for Vision Processing," at the December 2015 Embedded Vision Alliance Member Meeting. Trevett provides an update on recent developments in multiple Khronos standards useful for vision applications.

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Accelerating Machine Learning: Implementing Deep Neural Networks on FPGAs

This introductory article discusses implementing machine learning algorithms on FPGAs, achieving significant performance improvements at much lower power. Newly available middleware IP, together with the SDAccel programming environment, enables software developers to implement convolutional neural networks (CNNs) in C/C++, leveraging an OpenCL platform model. Machine Learning in the Cloud: A Tipping Point The transformation of …

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OpenCL Streamlines FPGA Acceleration of Computer Vision

The substantial resources available in modern programmable logic devices, in some cases including embedded processor cores, makes them strong candidates for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across programmable logic fabric and other heterogeneous processing elements within a system. As mentioned in the …

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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