Long availability

Carrierboard for COM-HPC-Client-Modules

26. Juli 2022, 16:00 Uhr | Tobias Schlichtmeier
Micro-ATX-Carrier-COM-HPC-Client
Congatec presents a Micro-ATX-compatible Carrierboard for COM-HPC.
© Congatec

Congatec brings a first Micro-ATX-compatible Carrierboard with COM-HPC interface into the market. It is made for industrial grade high end workstations and desktop clients. The board is designed for a long embedded availability of at least seven years.

Congatec's board is processor socket and manufacturer-independent and can be equipped with any Computer-on-Module (CoM) in COM-HPC Client Size A, B or C. It is scalable with Intel's 12th generation Core processors, which the company offers in 14 different variants. The performance options for the »conga-HPC/uATX« carrier board range from the »conga-HPC/cALS« Client Size C modules to »conga-HPC/cALP« Client Size A modules with Intel Celeron-7305E processor.

The free combination of CoM and Carrier enables future upgrade and update options of Micro-ATX based platforms. In times of uncertain supply chains, the ability to choose any available COM HPC module is a particular advantage. OEMs are thus not tied to a dedicated BGA or LGA processor from a single chip or CoM vendor, reducing the risk of supply bottlenecks. At the same time, the mechanics and application-specific peripherals can remain as they are without hardware changes. Application areas for Micro-ATX based system designs include system solutions that support multiple displays and are found in a variety of industries. Typical applications range from industrial and medical HMIs, real-time edge controllers, industrial PCs and control room systems to infotainment and digital signage applications.

Also implemented are dedicated AI engines that support Windows ML, the Intel distribution of the OpenVINO toolkit, and Chrome Cross ML. The various AI workloads can be seamlessly delegated to the P-cores, E-cores as well as the GPU execution units to handle even intensive edge AI workloads. Intel's integrated deep learning boost technology leverages the various cores via Vector Neural Network Instructions (VNNI). Integrated graphics also support AI-accelerated DP4a GPU instructions that even scale to dedicated GPUs.


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