Embedded vision is a subject that’s been in the news recently, as technological advances have enabled fast and accurate detection of a wide range of objects such as faces, pedestrians, and hand gestures at a very low power consumption. One such solution that’s driving this phenomenon comes in the form of the EV52 and EV54 vision processors, developed by Synopsys.
As part of the DesignWare EV family of vision processors, the two parts are fully programmable and configurable vision processor IP cores that, according to the company, “combine the flexibility of software solutions with the low cost and low power consumption of dedicated hardware.” The EV Processors implement a convolutional neural network (CNN) that can operate at more than 1,000 GOPS/W, enabling fast and accurate detection of a wide range of visual objects.
The EV Processors are designed to integrate seamlessly into an SoC. They can be used with any host processors and operate in parallel with the host. The family includes support for synchronization with the host through message passing and interrupts. In addition, the EV Processor memory map is accessible to the host. These features enable the host to maintain control while allowing all vision processing to be offloaded to the processor. The cores can access image data stored in a memory-mapped area of the SoC or from off-chip sources independently from the host through the ARM AMBA AXI standard system interface if required.
To speed application software development, the processors are supported by a comprehensive software programming environment based on existing and emerging embedded vision standards including OpenCV and OpenVX, as well as Synopsys’ MetaWare Development Toolkit. The OpenCV source libraries available for EV Processors provide more than 2,500 functions for real-time computer vision. The processors are programmable and can be trained to support any object detection graph. The OpenVX framework includes 43 standard computer vision kernels that have been optimized to run on the EV Processors, such as edge detection, image pyramid creation and optical flow estimation. Users can also define new OpenVX kernels, giving them flexibility for their current vision applications and the ability to address future object detection requirements.