Foxconn has combined its high-density, fan-less, and highly efficient edge computing solution, “BOXiedge", with Socionext’s "SynQuacer" SC2A11 parallel processor, and the Hailo-8 deep learning processor to provide energy efficient standalone AI inference nodes, benefiting applications including smart cities, smart medical, smart retail, and industrial IoT.
This joint solution will help to address the need for cost-effective multiprocessing capabilities required in video analytics, image classifications, and object segmentation. The product is capable of processing and analysing over 20 streaming camera input feeds in real-time, all at the edge. The result is a high-density, low-power, complete local VMS server, ensuring top performance for video analytics and privacy, including image classification, detection, pose estimation, and various other AI-powered applications - all in real time,
“Our vision at Foxconn is to pave the way for next generation AI solutions,” said Gene Liu, VP of Semiconductor Subgroup at Foxconn Technology Group. “We are confident that this strategic collaboration with our long-standing partner, Socionext, alongside Hailo, will do more than that. We recognise the great potential in adopting AI solutions for a multitude of applications, such as tumour detection and robotic navigation. Our edge computing solution combined with Hailo’s deep learning processor will create even better energy efficiency for standalone AI inference nodes to positively impact rapidly evolving sectors including smart cities, smart medical, smart retail, and industrial IoT.”
Foxconn has already deployed several in-house developed AI solutions on different production lines, leading to an improvement in reporting accuracy from 95% to 99% and a reduction of at least one third of the operating costs for appearance defect inspection projects.
“We are very pleased with this joint effort by the companies, and to officially announce our strategic partnership with Hailo,” said Noriaki Kubo, Executive Vice President at Socionext. “This collaboration will lead to more innovative solutions that specifically address the growing demand from our AI customers in multiple sectors. We are confident that this product will enable endpoint devices to operate with better performance, lower power, more flexibility, and minimal latency.”
Hailo’s specialized Hailo-8 deep learning processor delivers performance to edge devices. Featuring up to 26 Tera Operations Per Second (TOPS), the chip is built with an innovative architecture that enables edge devices to run sophisticated deep learning applications that could previously only run on the cloud. Its advanced structure translates into higher performance, lower power, and minimal latency, enabling enhanced privacy and better reliability for smart devices operating at the edge.
Commenting Orr Danon, CEO and Co-Founder of Hailo, said, “Our deep learning processor significantly upgrades the capabilities of smart devices operating at the edge, and this collaboration will impact a wide range of industries increasingly driven by edge technology. A new generation of chips means a new generation of capabilities at the edge.”
The next generation of the BOXiedge AI computing solution is equipped with applications for a broader market relying on low latency, a high data rate, high reliability, and quick processing at the edge.
Smart retail and smart cities, for instance, require hundreds of cameras - either in-store or in traffic monitoring - to generate video streams that need to be processed locally, quickly, and efficiently with minimal latency. Similarly, for industrial IoT, data acquiring, processing, inferencing, and presenting on the production floor rather than in the cloud translates into significant cost savings along with more efficient processing for tasks such as inspection and quality assurance.
