The integration of machine vision into industrial automation systems has a long history, gradually replacing manual inspection methods and significantly improving product quality and production efficiency. Over the years, we've witnessed the widespread adoption of cameras in everyday devices like computers, smartphones, and automobiles. However, the most significant breakthrough in machine vision has been the rapid advancement in processing power. As processor performance continues to double every two years, and technologies such as multicore CPUs and FPGAs gain more attention, vision system designers now have the ability to apply complex algorithms for data visualization and create smarter, more efficient systems.
Increased processing power allows designers to handle higher data throughput, faster image acquisition, and support for high-resolution sensors. They can also leverage new camera models with superior dynamic range. This improvement not only speeds up image capture but also enhances image processing capabilities. Preprocessing tasks like thresholding and filtering, as well as more complex operations like pattern matching, can be executed much faster. Ultimately, this enables quicker decision-making based on visual data than ever before.
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 incorporate multi-core CPUs and powerful FPGAs, it's crucial for designers to understand the trade-offs between these components. They must not only choose the right algorithms for the right hardware but also determine which architectures best suit their design goals.
When designing a heterogeneous vision system using both CPU and FPGA, there are two primary use cases: embedded processing and co-processing. In FPGA co-processing, the CPU captures images and sends them to the FPGA via direct memory access (DMA) for operations like filtering or color plane extraction. The processed image is then sent back to the CPU for advanced tasks such as OCR or pattern matching. Alternatively, all processing can occur on the FPGA, freeing up the CPU for other tasks like motion control or network communication.
In an embedded FPGA architecture, the camera interface connects directly to the FPGA, allowing pixel data to flow directly from the camera to the FPGA. This setup is commonly used with Camera Link cameras due to their compatibility with digital logic on FPGAs. It offers benefits such as offloading preprocessing from the CPU, reducing data volume, and enabling high-speed control operations within the FPGA itself.
Understanding how CPU and FPGA work together is essential when choosing the right platform for image processing. While CPUs process tasks sequentially, FPGAs can execute multiple operations in parallel, leading to significant speed improvements. For example, an algorithm that takes 24ms on a CPU might complete in just 6ms on an FPGA, even when accounting for data transfer times.
However, FPGAs aren't always the best choice. Their lower clock speeds compared to CPUs mean that for certain sequential algorithms, the CPU may outperform the FPGA. Additionally, FPGAs typically have less available memory, making them less suitable for applications requiring large-scale image alignment or template matching.
Programming FPGAs remains a challenge, as traditional development workflows involve lengthy compilation cycles. Tools like NI Vision Assistant help streamline this process by allowing developers to test and optimize algorithms on both CPU and FPGA platforms without getting bogged down by complex FPGA coding. These tools provide real-time feedback and performance metrics, accelerating the development cycle.
Ultimately, the choice between CPU and FPGA depends on the specific requirements of the application. Whether it's speed, accuracy, or resource constraints, each platform has its strengths. By understanding these trade-offs, designers can build more efficient and effective machine vision systems that push the boundaries of what’s possible in industrial automation.
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