ASUS IoT announces PE8000G

ASUS IoT has announced PE8000G at embedded world 2024, a powerful edge AI computer that supports multiple GPU cards for high performance – and engineered to handle rugged conditions with resistance to extreme temperatures, vibration and variable voltage. PE8000G is powered by formidable Intel Core processors (13th and 12th gen) and the Intel R680E chipset to deliver high-octane processing power and efficiency.

With its advanced architecture, PE8000G excels at running multiple neural network modules simultaneously in real-time – and represents a significant leap forward in edge AI computing. With its robust design, exceptional performance and wide range of features, PE8000G series is poised to revolutionise AI-driven applications across multiple industries, elevating edge AI computing to new heights and enabling organisations to tackle mission-critical tasks with confidence and to achieve unprecedented levels of productivity and innovation.

Dual-GPU power for seamless AI inferencing and imbued with industrial strength

PE8000G can support two graphics cards that each draw up to 450 watts, enabling redundancy, efficient high-throughput computing, seamless real-time AI inferencing and accelerated computing at the edge. In addition, PE8000G is able to handle a 8—48V DC-input range and offers built-in ignition power control and monitoring, for flexible power options in diverse deployment scenarios. Plus, it is engineered to adhere to exacting MIL-STD-810H military specifications for resistance to vibration and jolting. The fail-safe mechanism empowered by dual GPUs helps to provide accurate inference results even in challenging conditions, for reliability and confidence in AI-driven decision-making.

Optimised for computer vision and perception, and ready for the road

PE8000G is optimised for in-vehicle environments, featuring integrated ignition power control and power monitoring capabilities. It also excels in AI-driven factory automation, intelligent video analytics (IVA) and deployments in rugged environments such as roadside units (RSUs) and autonomous driving. Efficient pre-processing and perception capabilities can optimise data preparation and enhance the accuracy of AI inferencing.