FPGA application in edge computing

When it comes to autonomous driving, robot vision, and high-definition cameras, the camera unit is always at the forefront of discussion. Earlier, this article touched on some depth and stereo cameras used in FPGA-based applications for high-definition imaging and machine vision. The main role of FPGAs in these systems is to process the captured images. Image processing requires hardware that can handle parallel tasks efficiently, which is where FPGAs shine compared to traditional ARM CPUs. Today, we’re diving into how FPGAs are being applied in edge computing, a field that’s gaining momentum as more data needs to be processed locally rather than sent to the cloud. When talking about computing speed, cloud computing often comes to mind first. It offers massive computational power, fast processing, and scalability. However, there's a trade-off—cloud computing isn't always real-time. There's usually a delay when sending data to the cloud for processing, and waiting for results can be a problem in time-sensitive applications. That’s where edge computing steps in. Edge computing demands high performance and low latency, and FPGAs excel here due to their ability to process multiple tasks simultaneously. This makes them ideal for real-time applications like autonomous vehicles or smart city systems. This year, Aldec showcased its four-camera ADAS model based on the TySOM-2-7Z100 prototype board at the Embedded Vision Summit in Santa Clara, California. As shown in Figure 1, the TySOM-2-7Z100 demonstrates impressive performance, largely thanks to the Xilinx Zynq Z-7100 SoC inside. This device combines a dual-core ARM Cortex-A9 processor with an FPGA fabric, allowing for both software flexibility and hardware acceleration. Figure 2 shows the placement of the Zynq chip within the TySOM board. Why is the FPGA in Zynq so effective for edge computing? The Zynq-7000 Programmable SoC integrates a programmable ARM processor with an FPGA, enabling both software-based analysis and hardware acceleration. This combination allows for efficient image processing, where the FPGA handles the heavy lifting while the ARM core manages control and coordination. In the case of the TySOM-2-7Z100, the image processing pipeline starts with the camera capturing images, followed by edge detection algorithms. These algorithms analyze pixel data to identify object boundaries or lane lines. Processing this data on an ARM CPU would only allow for three frames per second. But with the FPGA, the system can process up to 27.5 frames per second—nearly ten times faster. This highlights the critical role of the FPGA in accelerating image processing tasks. The TySOM board is not just about processing power. It also includes a wide range of peripheral interfaces to interact with other devices. It supports up to 362 I/O ports, 16 GTX transceivers, and two FMC-HPC slots for expansion. For the ARM CPU, it includes standard interfaces such as DDR3 RAM, USB, and HDMI. The ARM core can run Linux or real-time operating systems, and it has 1GB of DDR3 RAM along with support for up to 32GB of SSD storage. Networking is facilitated through a Gigabit Ethernet port and four USB 2.0 interfaces. Meanwhile, the FPGA interacts with external devices via the two FMC-HPC sockets. This ensures that both the ARM core and the FPGA can communicate effectively with the outside world. The combination of these components makes the TySOM-2-7Z100 a powerful platform for edge computing applications. Autonomous driving is rapidly advancing, and as governments begin to recognize its potential, the technology will continue to evolve. Both hardware and software will play essential roles in shaping the future of smart cities and intelligent living. In this growing ecosystem, FPGAs will continue to move forward, adapting to new challenges and creating new opportunities for innovation.

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