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Dual Xilinx Ultrascale+ FPGA Accelerator for OTT Transcoding and Video Processing

  • High performance Xilinx Ultrascale+ FPGA (XCVU9P) x2
  • Aurora Link GTYx2 for FPGA interconnection
  • 32GB DDR4 memory in 4-Ch configuration w/ ECC support
  • PCIe Gen-3 x16 host interface
  • Full Height GPU Length form factor
  • Up to 150W slot power consumption
  • Fully Xilinx SDAccel supported
  • Integrated Intel® Enpirion® solution
The VEGA-4002 is a FPGA-based full height GPU length single deck PCI Express card which is ideal for accelerating live video streaming applications both in appliances and in scale-out data center servers. Reconfigurable FPGA-based video processing addresses OTT infrastructure users needs for evolving multi-codec encoding and transcoding as the demand for online streamed video content grows. In addition to established video codec standards like H.264/AVC and H.265/HEVC, OTT service providers now have to consider additional use cases for codecs like VP9 and potentially even AV1 in the near future. The latest generation of Field Programmable Gate Arrays (FPGAs) from Xilinx offers video processing acceleration while retaining future-proof reconfigurable capability;and Advantech’s new VEGA-4002, a dual Xilinx XCVU9P configuration, can provide access to this technology. VEGA-4002 can offer a higher density and more power efficient implementation for encoding and transcoding applications. The VEGA-4002 is fully supported by the Xilinx SDAccel development environment with FFMPEG integration. Advantech can also provide custom development support services for VEGA-4002 including FPGA IP provision and system integration, and the board can be delivered already preintegrated in a range of server platforms. Please contact your Advantech representative for more details.

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Featured Highlights

  • FPGA Platforms for Fastest Deep Learning Inference

    FPGA Platforms for Fastest Deep Learning Inference

    Advantech‘s VEGA Series of Xilinx FPGA-based PCI Express cards have been designed to accelerate AI deep learning workloads, offering a range of integration options to suit customer needs. In particular, the range focuses on the needs of video-based, real-time AI inference applications at the edge where fastest video processing is critical.