Breakthrough for deep learning with Intel FPGAs

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Intel and Chinese telecoms company ZTE claim to have achieved a new record – more than 1000 images per second in facial recognition – with what is known as ‘theoretical high accuracy’ achieved for their custom topology.

“Perception, such as recognising a face in an image, is one of the essential goals of the ZTE 5G system,” said Duan Xiangyang, vice president of the ZTE Wireless Institute. “Deep learning technology is important as it can enable such perception in mobile edge computing systems, thus making ZTE’s 5G system smarter.”

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.

According to the company, the deep learning designs can be migrated from the Arria 10 FPGA family to the Intel Stratix 10 FPGA family, and users can expect up to nine times performance boost. Besides increase in performance, the team at the ZTE Wireless Institute reduced design time by using 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,” commented Xiong Tiankui, chief engineer, ZTE Wireless Institute.

For more on convoluted networks, click here.