QNAP Mustang-F100

overview

Mustang-F100

Intel Vision Accelerator Design with Intel Arria 10 FPGA

As QNAP NAS evolves to support a wider range of applications (including surveillance, virtualization, and AI) you not only need more storage space on your NAS, but also require the NAS to have greater power to optimize targeted workloads. The Mustang-F100 is a PCIe-based accelerator card using the programmable Intel Arria 10 FPGA that provides the performance and versatility of FPGA acceleration. It can be installed in a PC or compatible QNAP NAS to boost performance as a perfect choice for AI deep learning inference workloads.

• Half-height, half-length, double-slot.
• Power-efficiency, low-latency.
• Supported OpenVINO toolkit, AI edge computing ready device.
• FPGAs can be optimized for different deep learning tasks.
• Intel FPGAs supports multiple float-points and inference workloads.

Available Models : Mustang-F100-A10-R10

PCIe FPGA Highest Performance Accelerator Card with Arria 10 1150GX support DDR4 2400Hz 8GB, PCIe Gen3 x8 interface

OpenVINO toolkit

OpenVINO toolkit is based on convolutional neural networks (CNN), the toolkit extends workloads across Intel hardware and maximizes performance. It can optimize pre-trained deep learning model such as Caffe, MXNET, Tensorflow into IR binary file then execute the inference engine across Intel-hardware heterogeneously such as CPU, GPU, Intel Movidius Neural Compute Stick, and FPGA.

Get deep learning acceleration on Intel-based Server/PC

You can insert the Mustang-F100 into a PC/workstation running Linux (Ubuntu) to acquire computational acceleration for optimal application performance such as deep learning inference, video streaming, and data center. As an ideal acceleration solution for real-time AI inference, the Mustang-F100 can also work with Intel OpenVINO toolkit to optimize inference workloads for image classification and computer vision.

• Operating Systems
  Ubuntu 16.04.3 LTS 64-bit, CentOS 7.4 64-bit, Windows 10 (More OS are coming soon)
• OpenVINO toolkit
  • IntelDeep Learning Deployment Toolkit
    • - Model Optimizer
    • - Inference Engine
  • Optimized computer vision libraries
  • IntelMedia SDK
    *OpenCL graphics drivers and runtimes.
  • Current Supported Topologies: AlexNet, GoogleNet, Tiny Yolo, LeNet, SqueezeNet, VGG16, ResNet (more variants are coming soon)
  • IntelFPGA Deep Learning Acceleration Suite
• High flexibility, Mustang-F100-A10 develop on OpenVINO toolkit structure which allows trained data such as Caffe, TensorFlow, and MXNet to execute on it after convert to optimized IR.

QNAP NAS as an Inference Server

OpenVINO toolkit extends workloads across Intel hardware (including accelerators) and maximizes performance. When used with QNAPs OpenVINO Workflow Consolidation Tool, the Intel-based QNAP NAS presents an ideal Inference Server that assists organizations in quickly building an inference system. Providing a model optimizer and inference engine, the OpenVINO toolkit is easy to use and flexible for high-performance, low-latency computer vision that improves deep learning inference. AI developers can deploy trained models on a QNAP NAS for inference, and install the Mustang-F100 to achieve optimal performance for running inference.

Note:

1. QTS 4.4.0 (or later) and OWCT v1.1.0 are required for the QNAP NAS.

2. To use FPGA card computing on the QNAP NAS, the VM pass-through function will be disabled. To avoid potential data loss, make sure that all ongoing NAS tasks are finished before restarting.

Easy-to-manage Inference Engine with QNAP OWCT

Specifications

FAQ