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Types of Processors Best Suited for Embedded Vision Applications

Embedded vision technology may be defined as a computer vision application that runs on a low-powered embedded system. One reason we are witnessing an explosion of embedded vision technology is due to the shrinking size of embedded vision processors. The algorithms needed for embedded vision are hungry for a processor’s resources. Speed, bandwidth, and throughput must be carefully balanced with cost and power consumption to create suitable processors for embedded vision.

Types of Processors for Embedded Vision Applications

Many computer vision applications rely on a central processing unit (CPU) and a graphics processing unit (GPU). As manufacturers have discovered the need for processors that are optimized for deep learning algorithms and that provide superior efficiency, they have started developing processors that can be paired with robust sensor technology to create even smaller systems.

Graphics processing units (GPUs) are designed to deliver large amounts of parallel computing power. They are well-suited for processing visual data right down to the individual pixel. General-purpose GPUs (GPGPUs) also use power efficiently while providing high performance.

Field programmable gate arrays (FPGAs) are growing in popularity for embedded vision applications. These devices have very low latency levels, which makes them a natural fit for computer vision pipelines. FPGAs provide the best of both worlds: algorithms and hardware in one package with lower power requirements than traditional CPUs or GPUs. They can also accelerate multiple portions of a computer vision pipeline simultaneously.

Architectures for Embedded Vision Processors

Embedded vision is changing how we interact with the world. It’s in our cameras, phones, and cars. To get embedded vision into your everyday devices, you need the right architecture—and you have several to choose from.

  • System-on-Chip (SoC)
    On a SoC, you have the CPU, GPU, interface controller, and often more all on a single chip. Your mobile device manufacturer is keenly interested in the development of SoCs.
  • System-on-Module (SoM)
    An SoM includes an SoC but adds RAM (random access memory), power management, and bus systems. A carrier board can add a power connection and additional connectivity. This is a design you might see on the production line at your manufacturing facility.

There are a couple more architectures to consider. The single-board computer (SBC) is essentially an SoM paired up with a carrier board in one. SBCs have low costs but can’t be easily customized to specific applications. One other architecture is fully custom designs. These designs are most commonly used in highly specific applications to reduce costs.

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