FPGAs and MPSoCs are ideally suited for machine vision applications due to their ability to process large amounts of data in parallel and at high speeds. FPGAs can run highly power efficient neural network implementations and benefit from ultra low latency connections to multiple image sensors. Given the inherent strengths of FPGAs for machine vision, it surprises me that GPUs have become the dominant hardware platform for deep learning applications1 in recent years. In my opinion, most of the potential for FPGAs in machine vision remains to be exploited.
[Read More]Camera FMC: Connecting MIPI cameras to FPGAs

