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Ken Lee, CEO of VanGogh Imaging, presents the "Using FPGAs to Accelerate 3D Vision Processing: A System Developer's View" tutorial within the "Implementing Vision Systems" technical session at the October 2013 Embedded Vision Summit East.

Embedded vision system designers must consider many factors in choosing a processor. This is especially true for 3D vision systems, which require complex algorithms, lots of processing power, and large amounts of memory. When VanGogh Imaging started development of its first 3D vision system three years ago for object recognition and measurement, it was based on a PC platform. Over the last two years, VanGogh Imaging has converted its software to run on an ARM-based system running Linux and Android. The conversion to the embedded system reduced cost but at the expense of performance, despite significant effort to reduce algorithmic and data structure complexity.

Now, in order to improve performance, the company has implementing the same design on an FPGA-SoC (Zynq from Xilinx). VanGogh Imaging's analysis has indicates that this approach will allow the company to increase performance dramatically with minimal additional cost. Ken Lee presents the new implementation approach and how it yields performance improvements, as well as lessons learned during the PC-to-ARM and ARM-to-SoC conversion process via design examples.

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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