Tim Hartley, Senior Product Manager in the Imaging and Vision Group at ARM, presents the "Computer Vision on ARM: The Spirit Object Detection Accelerator" tutorial at the May 2017 Embedded Vision Summit. In 2016, ARM released Spirit, a dedicated object detection accelerator, bringing industry-leading levels of power- and area-efficiency to computer vision workflows. In this […]
Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit. Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important,
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Tim Hartley, Product Manager in the Personal Mobile Compute Business Line at ARM, presents the "Lessons Learned from Bringing Mobile and Embedded Vision Products to Market" tutorial at the May 2016 Embedded Vision Summit. Great news: technology is finally at a point where we can build sophisticated computer vision applications that run on mass market
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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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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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™
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
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
Michael Tusch, Founder and CEO of Apical, presents the "Using Vision to Create Smarter Consumer Devices with Improved Privacy" tutorial at the May 2015 Embedded Vision Summit. Machines are valuable primarily due to their ability interact with people and with the physical world. But today, most of the consumer devices in the “Internet of Things”
Roberto Mijat, Visual Computing Marketing Manager at ARM, presents the "Understanding the Role of Integrated GPUs in Vision Applications" tutorial at the May 2015 Embedded Vision Summit. Today, systems-on-chip (SoCs) used in applications such as smart TVs, smartphones and tablets typically include a multi-core CPU with single-instruction, multiple-data capabilities as well as a GPU that
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