What Vision Engineers Might Not Know About FPGAs, a Powerful Processing Option
Most vision engineers are familiar with the two most common chipsets for machine vision applications: CPUs for general processing and GPUs for more compute-intense tasks. These two options address virtually any conventional application. But as industrial frameworks and automation demand more from vision systems, engineers might need to look beyond their familiar toolkit and consider a processing option that often gets overlooked. FPGAs have been quietly evolving, and for specific vision challenges that involve real-time processing, tight space constraints, or unique I/O requirements, they’re worth a serious look.
The use of FPGAs can benefit even common applications like barcode reading and optical character recognition (OCR), which often rely on machine vision software tools to optimize images by adjusting gain, contrast stretching, sharpening or denoising operations. Pairing a machine vision camera with an FPGA allows computationally intensive software tools to complete tasks more efficiently and quickly. Here, two examples each of unprocessed barcode images (left), enhanced images (middle), and final binary images ready for reading (right). The enhancements to the middle images show the results of histogram equalization, sharpening, and denoising. The two rightmost images add binarization.
Today’s Processing Landscape
Understanding when to choose each type of processor comes down to matching the tool to the task. CPUs excel as system orchestrators — handling operating systems, user interfaces, and general application logic. Most developers are conversant with the extensive software libraries and straightforward programming models that today’s CPUs offer.
GPUs shine in applications that need massive parallel processing power, such as training neural networks or handling compute-intensive algorithms across large datasets. The thousands of cores they embed make these chips ideal for tasks that need to be broken into many simultaneous operations.
FPGAs take an entirely different approach. Think of them as programmable hardware — you’re essentially creating custom circuits optimized for your specific processing pipeline. Instead of running software instructions sequentially, FPGAs can execute multiple operations simultaneously through dedicated logic paths. Their unique architecture makes them particularly valuable in applications that require deterministic timing and ultra-low latency.
FPGA Value Propositions for Vision
What makes FPGAs compelling for machine vision deployments?
First, they are an effective alternative where physical space is at a premium, such as with embedded systems or edge devices. FPGAs can often handle multiple processing tasks that would otherwise require separate components. For example, you might replace a CPU–GPU combination with a single, more compact FPGA solution.
FPGAs also deliver lower latency. Because they process data through dedicated hardware paths rather than relying on software instructions, they provide consistent, predictable response times. This deterministic quality is valuable in vision systems where timing variability can be problematic.
Calculating the cost of a vision processor depends on several factors, but FPGAs can sometimes offer a lower total cost of ownership where they enable reduced component counts, lower power consumption, or simplified thermal management. The development investment of FPGAs is higher upfront, but the hardware costs can be lower in volume production.
Where Do FPGAs Make the Most Sense?
In general, FPGAs are the most suitable option for compute-intensive applications. For example, they are appropriate for high-speed PCB inspection systems, where defects such as unwanted solder bridges must be detected and flagged within microseconds to enable real-time process correction. They are also appropriate for multi-camera embedded systems handling 10GigE+ data streams that could overwhelm traditional processing approaches. Another example is laser welding applications that require instantaneous feedback loops.
All these scenarios share common threads: tight timing requirements, high data throughput, and the need for immediate decision-making unimpeded by operating system scheduling or memory transfers that can introduce unpredictable delays.
When your application demands guaranteed response times and can benefit from hardware-accelerated processing pipelines, FPGAs deserve consideration alongside more conventional options.
Concurrent EDA specializes in creating high-performance FPGA designs directly from customer software. If you would like to learn more about how FPGAs can drive advances in your high-speed, high-data-rate machine vision applications, reach out to set up a meeting here.
To dive deeper into the comparative benefits of FPGAs versus other image processors, click below to read our companion post: “How Evolving FPGA Architectures Will Transform Machine Vision Applications.”
Distributed by Concurrent EDA, LLC
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