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    FX-Series FPGA Accelerator Cards

    Huawei’s FX-Series FPGA accelerator cards unlock the ultimate performance with 16 x PCIe 3.0 interconnect lanes and two 100 GE network ports. The cards use the Xilinx UltraScale+ 16-nm VU9P and VU5P chips and support up to 32 GB of external memory. FX-Series cards are optimized for workloads such as Artificial Intelligence (AI), gene sequencing, video encoding, image processing, data compression, and network processing. They deliver compelling compute power and large-bandwidth data connections for various types of hardware-accelerated services and help turbocharge data centers with optimal energy efficiency.

    Application Scenarios

    • FPGA

      Video Processing

      Video applications, such as automatic image classification and recognition, image search, transcoding, real-time rendering, livecasting, plus augmented and virtual reality, require high real-time computing performance that can be provided by FPGA servers. Offering cost-effective video solutions and ideal for video scenarios, they reduce H.265 encoding deployment costs by 40%, improve H.265 encoding compression rate by 30% to 50% over H.264, and reduce HD livecast expenditure by 30% to 50% for Content Delivery Networks.

    • Images Codecs

      Image Transcoding

      Expanding image content continues with the rapid development of technologies such as big data, the IoT, mobile interconnection, and cloud computing and consumes a large amount of server resources. The FPGA server features advantages such as efficient parallel computing, high throughput, and low latency, and is ideal for solving issues such as low CPU processing concurrency, slow image processing, and heavy computing resource consumption. TCO is down to 1/7 of the original, performance is improved by 15x, and latency is slashed to 1/3.

    • FPGA

      Genome Research

      Precision medicine can be achieved through gene sequencing and analysis as well as rapid analysis of mass biological and medical data. Many fields, such as pharmaceutical development and molecular breeding, also require mass data processing and hardware acceleration to resolve performance bottlenecks for biological computation. FPGA servers meet such requirements with their outstanding programmable hardware computing performance that is up by 6x. CAPEX is down by 40%, deployment space is reduced by 80%, and power consumption is down by 70%.

    • FPGA

      Financial Analytics

      The financial industry has stringent requirements on computing capabilities, and demands ultra-low latency and high-throughput rapid response. Service scenarios include financial computing based on the pricing tree model, high-frequency financial transactions, fund and securities transaction algorithms, financial risk analysis and decision-making, plus transaction security assurance. The FPGA server provides an optimal hardware acceleration solution for these scenarios by using programmable hardware acceleration technology. In certain scenarios, the FPGA server delivers 100x performance improvement above that achieved by software. FPGA servers also boosted computing performance and analysis accuracy, as well as achieved ultra-low latency through customized hardware circuits.

    • Deep Learning

      Multi-layer neural networks in machine learning require a large volume of computing resources. The training process involves mass data management, while the inference process requires ultra-low latency. In addition, machine learning algorithms are continuously improving. FPGA accelerator cards meet these requirements due to their parallel computing, programmable hardware, low power, and low latency. FPGA accelerator cards dynamically provide the optimal hardware circuit design for various machine learning algorithms, meeting the strict requirements for mass computing and ultra-low latency. Therefore, FPGA accelerator cards serve as a compelling solution to meet the hardware requirements of machine learning. With FPGA cards, throughput is improved by 1.5x and latency is reduced by half.

    Huawei

    Specifications

    Model FX600 FX300
    Form Factor Full-height, 3/4-length Half-height, half-length
    FPGA Xilinx UltraScale+ 16-nm VU9P
    2,586K LE, 6,800 DSP
    345.9 Mbit/s
    Xilinx UltraScale+ 16-nm VU5P
    1,314K LE, 3,474 DSP
    168.2 Mbit/s
    PCIe 16 x PCIe 3.0 16 x PCIe 3.0
    Memory 4 DDR4 DIMMs, 8 GB/16 GB 2,400 MHz 64-bit SDRAM ECC 2 DDR4 DIMMs, 16 GB 2,133 MHz 64-bit SDRAM ECC
    Network 2 QSFP28 100 GE ports, compatible with 40 GE 2 QSFP28 100 GE ports
    Power Supply Passive cooling
    Up to 200W
    Passive cooling
    Up to 75W
    Indicators/Buttons Status indicator
    Optical port status indicator
    Golden self-healing button
    Status indicator
    Optical port status indicator
    Golden self-healing button
    Configuration FPGA configuration via JTAG
    1 Gbit/s QSPI flash memory
    FPGA configuration via JTAG
    1 Gbit/s QSPI flash memory
    Temperature 0°C to 45°C (32°F to 113°F) 0°C to 45°C (32°F to 113°F)

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  • 达索系统3D体验高峰论坛暨广州智能制造大会召开 2019-05-18
  • 行摄最美乡村丹巴“美人谷”文章中国国家地理网 2019-05-18
  • 国宝级黄腹角雉住进三清山 2019-05-07
  • 新疆旅游推介会亮相北京 2019-05-07
  • 办过周永康、孙政才、令计划案的检法机关,拟双双换帅 2019-05-05
  • 中船重工总经理孙波涉嫌严重违纪违法接受中央纪委国家监委审查调查 2019-05-05
  • 人民网评:当好马克思主义的忠诚信奉者、坚定实践者 2019-05-04
  • 江苏泰兴长江边倾倒污泥“变本加厉” 2019-05-04
  • 内乱困扰西班牙 C罗亟须正名 2019-04-29
  • 一个企业里的劳动力,有可能被叫做职工,意指按职责做事的人,也可能被叫做劳工,意指按劳动力做事的人。不同的称呼,体现了不同的企业性质与追求。懂这点,你才明白这“意 2019-04-02
  • 全论坛的人都跟你一样不会算8421,把自己的祖宗十八代都算没了?[大笑] 2019-04-02
  • 国务院部署实施蓝天保卫战三年计划 2019-03-28
  • 陈小林的专栏作者中国国家地理网 2019-03-28
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  • 人民网春季糖酒会专访双汇发展副总裁杜俊甫 2019-03-23
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