Abstract
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 Cameras 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 manycore 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 eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.
Highlights
The emergence of new trends in technology, such as the Internet of Things and Industry 4.0, pulled out several applications based on image processing and computer vision (IP/CV) techniques
We selected a ZYNQ Ultrascale+ device (ZCU104 development kit from Xilinx), a state-of-art SoC [22], which integrates a general-purpose processor (GPP) with an field-programmable gate array (FPGA) fabric in the same chip
All data use the QVGA image resolution with the FPGA running at 100 MHz, and the ARM processor running at 667 MHz
Summary
The emergence of new trends in technology, such as the Internet of Things and Industry 4.0, pulled out several applications based on image processing and computer vision (IP/CV) techniques. Most of the conventional cameras are designed for the acquisition and transmission of images and videos. These cameras are not able to support complete applications running under real-time constraints. For these reasons, there is a need for devices capable of acquiring and processing images and videos efficiently and in real-time. The literature shows that for the IP/CV domain, the efficient parallelism exploration is the key for performance improvement. To explore the parallelism massively, our approach was to parallelize the processing right after image capture by the pixel sensor.
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