This is an unusually high execution performance for an embedded device – compared to a standard PC. Users can thus enjoy all the benefits of deep learning on the popular NVIDIA Jetson TX2 embedded board. This is possible thanks to the availability of two pretrained networks that MVTec ships with HALCON 17.12. One of them (the so called "compact" network) is optimized for speed and therefore ideally suited for use on embedded boards. MVTec will provide interested customers with a software version for this architecture on request.
Take advantage of the benefits of a standard library
In addition to deep learning, the full functionality of the standard machine vision library HALCON is available on these embedded devices. Applications can be developed on a standard PC. With the help of HDevEngine, the trained network as well as the application can then subsequently be transferred to the embedded device. Plus, users can utilize more powerful GPUs, available for the PC, to train their CNN, and then execute the inference on the embedded system. This shortens time to market.
“We have provided successful technological proof that allows us to offer advanced deep learning functions in the embedded vision segment. This will greatly benefit users. They can now utilize the extensive new HALCON 17.12 features on standard devices with NVIDIA Pascal architecture – at an extraordinary high speed for embedded technologies,” explains Christoph Wagner, MVTec's Embedded Vision Product Manager.
Meeting the exact new market requirements
Dr. Olaf Munkelt, Managing Director of MVTec Software GmbH, adds, “The rapidly growing market for embedded systems requires corresponding high-performing technologies. At the same time, AI-based methods such as deep learning and CNNs, are becoming more and more important in highly automated industrial processes. We are specifically addressing these two market requirements by combining HALCON 17.12 with the NVIDIA Pascal architecture.”