Foxconn, Socionext and Hailo partner on edge AI

Foxconn, Socionext and Hailo partner on edge AI

Business news |
By Julien Happich

Foxconn has combined its high-density, fan-less, and highly efficient edge computing solution, “BOXiedge”, with Socionext’s high-efficiency parallel processor “SynQuacer” SC2A11, and the Hailo-8 deep learning processor. This joint solution helps address the need for cost-effective multiprocessing capabilities required in video analytics, image classifications, and object segmentation. The robust, high-efficiency product is capable of processing and analyzing 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.
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.
Hailo’s specialized Hailo-8 deep learning processor delivers unprecedented 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.
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, where every split-second counts, 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.
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