A manycore vision processor architecture for embedded applications

Real-Time Image Processing and Computer Vision systems are now in the mainstream of technologies enabling applications for Cyber-Physical Systems, Internet of Things, Augmented Reality, and Industry 4.0. These applications bring the need for Smart Camera for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a many-core vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed Processing Elements and Memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to four hundred Processing Elements were implemented and compared to several works from the literature. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.