Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN).
Developed with ZTE, a leading technology telecommunications equipment and systems company, the image recognition technology is what many companies in Internet search and AI are trying to advance.
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 their 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. The benchmark was achieved on a server holding 4S Intel Xeon E5-2670v3 processors running at 2.30GHz, 128GB DDR4; Intel PSG Arria 10 FPGA Development Kit with one 10AGX115 FPGA, 4GB DDR4 SODIMM, Intel Quartus Prime and OpenCL SDK v16.1.
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.