This teaching-oriented paper presents a structured introduction to the deployment of hardware-accelerated vision AI applications on the Xilinx Kria KV260 platform. Rather than focusing on low-level hardware design, the material emphasises system integration, runtime configuration, and the interaction between software and reconfigurable logic within a modern heterogeneous embedded system. Using a pre-integrated smart camera application as a guiding example, the paper demonstrates how FPGA-based acceleration can be accessed through standard Linux tools, containerised execution environments, and network-based video streaming. The approach is specifically designed for higher education, enabling students to gain practical insight into adaptive computing platforms while maintaining a clear conceptual understanding of hardware–software co-design principles.
Jörg Cosfeld (Thu,) studied this question.