The field of real-time signal processing and machine learning is moving towards the development of more efficient and accurate systems. Researchers are exploring the use of Field Programmable Gate Arrays (FPGAs) to accelerate complex algorithms and improve performance. This is evident in the development of novel methodologies for accelerating Capsule Networks on FPGAs, which have shown significant improvements in image understanding and generalization ability. Additionally, there is a growing trend in using FPGAs for real-time computer vision applications, such as object detection, classification, and tracking. Noteworthy papers in this area include FastCaps, which proposes a novel two-step approach for deploying a full-fledged CapsNet on FPGA, and CapsBeam, which presents a capsule network based beamformer for ultrasound non-steered plane wave imaging on FPGA. These advancements have the potential to enable highly performance-efficient deployment of machine learning models on low-cost FPGAs, making them suitable for edge devices and real-time applications.