Inspur Information Showcases Leading Advances in AI Servers and Research at CVPR22

Inspur Information, a leading provider of AI solutions, will attend the Computer Vision and Pattern Recognition (CVPR) conference showcasing its advanced AI capabilities and end-to-end solutions, from servers to software. CVPR is a premier annual AI conference that includes presentations, workshops, courses, and expos for students, academics, researchers, and professionals. The expo features the largest gathering of industry leaders in the field of computer vision, machine learning, and AI.

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“Scene Representation in Bird’s-Eye View from Surrounding Cameras with Transformers”

On display at the expo are Inspur Information’s high-performance AI solutions designed to accelerate scientific discovery and empower technology innovation. AI training servers NF5488A5, NF5688M6, and NF5448A6 feature the latest Intel Xeon Scalable and AMD EPYC processors as well as NVIDIA A100 Tensor Core GPUs to deliver record-breaking compute performance for the most data-intensive training workloads. NF5280M6 and NF5468A5 provide flexible PCIe Gen4 interfaces to support a variety of configurations. Inspur Information’s OVX solution for digital twins, based on the NVIDIA-Certified NF5468M6 server, supports the data-intensive applications, workflows, and real-time 3D design collaboration capabilities of NVIDIA Omniverse Enterprise.

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The server is accompanied by a demo of the NVIDIA OVX reference architecture. Demonstrating Inspur Information’s innovations in open computing is a 21-inch OCP-compliant open AI accelerator system – the MX1 – supporting Habana Gaudi and other AI chips to enable deep learning on open compute architectures. On the software side, AIStation offers an all-in-one AI resource management platform from development and training to deployment and inference.

Inspur Information will also contribute two research papers to the conference. “CoDo: Contrastive Learning with Downstream Background Invariance for Detection” proposes a contrastive learning framework that improves consistency in image classification and object detection. The other thesis, “Scene Representation in Bird’s-Eye View from Surrounding Cameras with Transformers,” describes a machine learning model and framework used to transform camera image input to enhance environment perception in autonomous driving.

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