What are some common challenges faced by computer vision FPGA engineers when optimizing algorithms for hardware implementation?

Computer Vision FPGA engineers often encounter challenges related to balancing processing speed, resource utilization, and power consumption when implementing algorithms on hardware. Translating complex computer vision algorithms to run efficiently on FPGAs requires a deep understanding of hardware constraints and the ability to optimize code for parallel processing. Additionally, these engineers frequently collaborate with software developers, data scientists, and hardware teams to adapt algorithms for real-time performance while ensuring accuracy and scalability, which can involve extensive iteration and testing.