FPGA application in edge computing

When it comes to autonomous driving, robotics, and high-definition imaging systems, the camera unit is always at the forefront of discussion. Earlier, this article touched on depth cameras and stereo vision systems used in FPGA-based applications for HD cameras and machine vision. The main role of FPGAs in these systems is image processing, which demands hardware capable of handling parallel tasks efficiently. While ARM CPUs are widely used, they don’t match the speed and performance of FPGAs when it comes to real-time image processing. Today, we’re diving deeper into how FPGAs are being leveraged in edge computing. Computing speed often brings cloud computing to mind—known for its powerful processing capabilities, large data storage, and fast computation. However, cloud computing isn't without drawbacks. One major limitation is latency; there's always a delay when sending data to the cloud for processing, which can be critical in time-sensitive applications. That’s where edge computing shines. It requires high computational power and ultra-low latency, making it ideal for real-time applications. FPGAs excel here due to their ability to process multiple tasks simultaneously, offering both speed and real-time performance. This makes them a perfect fit for edge computing scenarios. At the Embedded Vision Summit in Santa Clara, California, Aldec showcased the TySOM-2-7Z100 prototype board, an advanced four-camera ADAS model. This board delivers impressive performance, thanks to the Xilinx Zynq Z-7100 SoC at its core. The Zynq device combines a dual-core ARM Cortex-A9 processor with programmable FPGA logic, enabling efficient image processing and hardware acceleration. This hybrid architecture allows for flexible and high-performance computing, especially in demanding environments like autonomous driving. Figure 1 shows the TySOM-2-7Z100 prototype board, while Figure 2 highlights the placement of the Zynq chip within the system. The integration of the ARM core and FPGA in one chip enables seamless coordination between software and hardware, optimizing both performance and efficiency. In the case of image processing, the FPGA handles computationally intensive tasks like edge detection, which involves analyzing millions of pixels per frame. When processed by the ARM CPU alone, only three images per second could be handled. But with the FPGA, the rate jumps to 27.5 images per second—nearly a tenfold increase in speed. Beyond raw processing power, the TySOM-2-7Z100 also features extensive peripheral interfaces. It supports up to 362 I/O ports, 16 GTX transceivers, and two FMC-HPC connectors for expansion. The ARM core manages standard interfaces such as DDR3 RAM, USB, and HDMI, while also supporting Linux and other real-time operating systems. With 1GB of DDR3 memory and support for up to 32GB of SSD storage, the system is highly scalable. Network connectivity is achieved via a Gigabit Ethernet port and four USB 2.0 interfaces, while the FPGA interacts with external devices through the FMC-HPC sockets. Autonomous driving is rapidly advancing, and as governments begin to recognize its potential, the technology is gaining momentum. Both hardware and software will continue to evolve, shaping the future of smart cities and intelligent living. In this growing landscape, FPGAs are positioned to play a key role, adapting to new challenges and opening up new opportunities. As the demand for real-time, high-performance computing increases, the development of FPGA technology will keep pace, ensuring it remains a vital component in the ever-changing tech ecosystem.

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