How are FPGAs Used in Embedded Vision System Applications?

Field programmable gate arrays (FPGA) have emerged as an ideal processor in embedded vision systems in a wide range of applications. FPGAs are semiconductor devices are built around a number of configurable logic blocks (CLB). These CLBs are connected via programmable interconnects, allowing them to be reprogrammed as desired after manufacturing.

FPGAs differ from other types of processors, such as application specific integrated circuits (ASIC), because of their ability to be programmed after manufacturing, among other performance attributes. In the embedded vision world, FPGAs offer many benefits for end users.

Why are FPGAs Valuable in Embedded Vision Systems?

For embedded vision systems, FPGAs offer much-needed flexibility and performance to support overall system functionality and viability in many applications. The most advanced FPGAs have the performance and low power consumption of ASIC devices, yet still retain the flexibility and time-to-market advantages of other processors such as graphics processing units (GPUs) or digital signal processors (DSPs).

Modern embedded vision applications often require on the spot imaging tweaks. Over time, capabilities advance dramatically. The flexibility of FPGAs to be reconfigured and reprogrammed are highly desirable for embedded vision users to facilitate these changes in vision system performance. Additionally, FPGAs often feature simplified design considerations for many different image sensors.

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What Kinds of Applications are FPGAs Used For?

Embedded vision systems with FPGA processors benefit from enhanced processing capabilities. This allows them to tackle demanding and intelligent vision tasks, such as detecting the presence of road signs. They may also be used to set up virtual trip wires to detect and track objects that move within a certain portion of a video frame.

FPGAs can also help facilitate the implementation of machine learning in AI-based edge computing applications. They can be particularly useful in always-on, inferencing applications where AI systems begin to extend their knowledge by identifying patterns – a highly resource-intensive process that benefits greatly from the parallel processing capability of FPGAs.

In embedded vision systems, FPGAs have emerged as a robust processing solution for their flexibility and performance in a wide range of embedded vision applications. As embedded vision systems grow in popularity, FPGAs will be increasingly relied on for powerful image processing.