The application of machine vision in industrial automation systems has a long history, gradually replacing traditional manual inspection methods. This transition has significantly improved production quality and output efficiency. Over the years, we've witnessed the widespread integration of cameras into everyday devices such as computers, smartphones, and vehicles. However, the most significant advancement in machine vision has been in processing power. As processor performance continues to double every two years, and technologies like multi-core CPUs and FPGAs gain more attention, vision system designers now have the capability to implement complex algorithms that enhance data visualization and create smarter, more efficient systems.
With increased processing power, designers can achieve higher data throughput, allowing for faster image acquisition and the use of high-resolution sensors. They can also take advantage of modern cameras with superior dynamic range. Improved performance not only speeds up image capture but also accelerates image processing. Tasks such as thresholding, filtering, or pattern matching can be executed more efficiently. Ultimately, this enables designers to make quicker decisions based on visual data, improving overall system responsiveness.
Brandon Treece, Director of Data Acquisition and Control Products at NI Headquarters in Austin, Texas, who oversees machine vision initiatives, emphasizes that as vision systems increasingly integrate advanced multi-core CPUs and powerful FPGAs, it's crucial for designers to understand the trade-offs between these components. It's not just about running the right algorithm on the right hardware, but also choosing the best architecture for their specific design needs.
When building a heterogeneous vision system using both CPU and FPGA, two main approaches are commonly considered: embedded processing and co-processing. In co-processing, the CPU and FPGA work together, sharing the workload. This is especially common with GigE Vision and USB3 Vision cameras, where the CPU handles image acquisition while the FPGA performs tasks like filtering or color plane extraction. The processed image can then be sent back to the CPU for further operations such as OCR or pattern matching.
In some cases, all processing steps can be handled by the FPGA, with only the final results sent back to the CPU. This frees up CPU resources for other tasks like motion control or network communication. On the other hand, embedded processing involves connecting the camera directly to the FPGA, which is often used with Camera Link cameras due to the ease of implementing acquisition logic on digital circuits.
This approach offers two major benefits: first, it allows for pre-processing on the FPGA, reducing the amount of data the CPU must handle. Second, it enables high-speed control operations within the FPGA itself, making it ideal for applications like real-time sorting or classification.
Understanding how CPUs and FPGAs process images is essential for optimizing performance. While CPUs execute operations sequentially, FPGAs can perform multiple operations in parallel, drastically reducing processing time. For example, an algorithm that takes 24 ms on a CPU could be completed in just 6 ms on an FPGA, even when including data transfer times.
Real-world examples, such as particle counting, demonstrate this advantage. Using a convolution filter, thresholding, and morphology, an algorithm that takes 166.7 ms on a CPU can be completed in just 8 ms on an FPGA—over 20 times faster. However, FPGAs aren't always the best choice. If an algorithm requires iteration and lacks parallelism, a CPU might outperform an FPGA.
Despite these advantages, FPGA-based systems come with challenges, particularly in programming complexity. Developing algorithms for FPGAs requires careful consideration of latency, memory, and synchronization. To streamline development, tools like NI Vision Assistant allow developers to test and optimize algorithms on both CPU and FPGA platforms without getting bogged down by compilation delays.
Ultimately, the choice between CPU and FPGA depends on the specific requirements of the application. Whether speed, accuracy, or cost is the priority, the combination of CPU and FPGA architectures can elevate machine vision systems to new levels of performance and reliability.
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