FPGAs have proven to be a compelling solution for solving deep learning problems, particularly when applied to image recognition. The advantage of using FPGAs for deep learning is primarily derived from several factors: their massively parallel architectures, efficient DSP resources, and large amounts of on-chip memory and bandwidth. An illustration of a typical FPGA architecture […]
FPGAs for Deep Learning-based Vision Processing Read More +
Neural networks enable the identification of objects in still and video images with impressive speed and accuracy after an initial training phase. This so-called "deep learning" has been enabled by the combination of the evolution of traditional neural network techniques, with one latest-incarnation example known as a CNN (convolutional neural network), by the steadily increasing
Deep Learning for Object Recognition: DSP and Specialized Processor Optimizations Read More +
This article was originally published by Embedded Vision Alliance consultant Dave Tokic. It is reprinted here with Tokic's permission. It seems to me that hardly a day goes by without some mention of self-driving cars (mostly good, some tragic), drones that follow you to record your ski run, augmented and virtual reality goggles that immerse
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,
Optimizing Computer Vision Applications Using OpenCL and GPUs Read More +
This article was originally published at Imagination Technologies' website. It is reprinted here with the permission of Imagination Technologies. It was clear last week at the annual Embedded Vision Summit in Santa Clara that the time of computer vision and deep learning on mobile had finally arrived. Interest in the area is growing noticeably –
Deep Learning on Mobile Devices at the Embedded Vision Summit 2016 Read More +
This article was originally published at CEVA's website. It is reprinted here with the permission of CEVA. Demand is on the rise for video cameras on moving platforms. Smartphones, wearable devices, cars, and drones are all increasingly employing video cameras with higher resolution and higher frames rates. In all of these cases, the captured video
Video Stabilization Using Computer Vision: Tips and Insights From CEVA’s Experts Read More +
This article was originally published by Embedded Vision Alliance consultant Dave Tokic. It is reprinted here with Tokic's permission. My biggest “aha moment” out of many during the Embedded Vision Summit 2016 came right at the beginning. I was a front row participant as moderator of the 2-day Business Insights track and have been closely
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. 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
OpenVX, a maturing API from the Khronos Group, enables embedded vision application software developers to efficiently harness the various processing resources available in SoCs and systems. Vision technology is now enabling a wide range of products, that are more intelligent and responsive than before, and thus more valuable to users. Such image perception, understanding, and
OpenVX Enables Portable, Efficient Vision Software Read More +
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
Optimizing Fast Fourier Transformation on ARM Mali GPUs Read More +
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