Chip maker Intel and Chinese mobile ZTE say a deep learning system the two have worked on has successfully processed more than 1000 images per second in facial recognition.The test took place in Nanjing, where ZTE's engineers used Intel's Arria 10 FPGA for a cloud inferencing application using a convolutional neural networks (CNN) algorithm.”Perception, such as recognizing a face in an image, is one of the essential goals of the ZTE 5G System,” said Duan Xiangyang, vice president of ZTE Wireless Institute. “Deep learning technology is very important as it can enable such perception in mobile edge computing systems, thus making ZTE's 5G System smarter.”ZTE has achieved a new record – beyond a thousand images per second in facial recognition – with what is known as “theoretical high accuracy” achieved for its custom topology. Intel's Arria 10 FPGA accelerated the raw design performance more than 10 times while maintaining the accuracy.The Arria 10 FPGA provides up to 1.5 teraflops (TFLOPs) single precision floating-point processing performance, 1.15 million logic elements and more than a terabit-per-second high-speed connectivity.Such deep learning designs can be seamlessly migrated from the Arria 10 FPGA family to the high-end Intel Stratix® 10 FPGA family, and users can expect up to nine times performance boost.Besides the impressive increase in performance, the team at ZTE Wireless Institute sped design time with the use of the OpenCL programming language.”With the Intel reference design, and using the Intel SDK for OpenCL to program the FPGA, our development time was greatly shortened,” said Xiong Tiankui, chief engineer at ZTE Wireless Institute. “We are pleased with the benchmark achieved and thank the Intel Programmable Solutions Group for supporting our project.”

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