Faster GPUs mean better machine vision with new OnLogic ruggedized systems

OnLogic has announced the availability of the new Cincoze GM-1000, a rugged embedded computer, configurable with MXM form factor GPUs from NVIDIA and AMD.

The announcement notes the system is designed for equipment manufacturers, system integrators, computer vision solution providers, and end customers who need powerful, reliable computing and graphics processing for machine vision, security, autonomous vehicles, medical imaging or edge AI applications.

According to BCC Research, the global market for machine vision is expected to reach $31.1 billion by 2024, with a CAGR of 9.7%. Machine vision is widely used in industrial and manufacturing applications ranging from object positioning, inspection, measurement, identification, and sorting. The GM-1000 utilizes a combination of CPU and GPU computing for Artificial Intelligence of Things (AIoT) and machine learning tasks.

“Utilizing traditional fan-cooled graphics cards for machine vision and image processing in harsh environments has previously required a compromise,” explains OnLogic Rugged Line Product Manager Maxx Garrison. “The required graphics processing capabilities meant users were forced to settle for solutions with fewer environmental protections. Now, with the GM-1000 and its passively cooled MXM GPUs, our customers can take advantage of GPU-accelerated computing in a truly rugged system.”

Three key features of GM-1000 with direct benefit to machine vision applications and innovators are: superior performance with high reliability, high-speed I/O and versatile expansion, and small footprint and ruggedization.

The Cincoze GM-1000 can be configured with a wide range of storage, memory options, and OS options, including Windows and Linux. The system is now available to configure and purchase.

OnLogic Cincoze GM-1000

Article Topics

 |   |   |   | 


Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Featured Edge Computing Company

Edge Ecosystem Videos

Machine learning at the Edge


Latest News