This project presents an FPGA architecture for the computation of visual attention based on the combination of a bottom-up saliency and a top-down task-dependent modulation streams. The bottom-up stream is deployed including optical flow, local energy, red-green and blue-yellow color opponencies, and different local orientation maps. The final saliency is modulated by two highlevel features: optical flow and disparity. The architecture include some feedback masks to adapt the weights of the features that are part of the bottom-up stream, depending on the specific target application. The target applications are ADAS (Advanced Driving Assistance Systems), video surveillance, robotics, etc...