ZTE Touts Deep
Learning Breakthrough with Intel FPGAs
February 8, 2017
Intel and ZTE have worked together to
reach a new benchmark in deep learning and convolutional neural networks
(CNN). The technology is what many companies in Internet search and
artificial intelligence are trying to advance, and includes picture
search and matching, as one example.
“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.”
The test took place in Nanjing City, China, where ZTE’s engineers used
Intel’s midrange Arria® 10 FPGA for a cloud inferencing application
using a CNN algorithm.
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
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.
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
“With the Intel reference design, and using the Intel SDK for OpenCL to
program the FPGA, our development time was greatly shortened,” said
Xiong Xian Kui, chief engineer at ZTE Wireless Institute. “We are
pleased with the benchmark achieved and thank the Intel Programmable
Solutions Group for supporting our project.”