FPGA AI Suite: PCIe-based Design Example User Guide

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ID 768977
Date 7/31/2024
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Document Table of Contents
Document Table of Contents x
1. FPGA AI Suite PCIe-based Design Example User Guide 2. About the PCIe* -based Design Example 3. Getting Started with the FPGA AI Suite PCIe* -based Design Example 4. Building the FPGA AI Suite Runtime 5. Running the Design Example Demonstration Applications 6. Design Example Components 7. Design Example System Architecture for the Agilex™ 7 FPGA A. FPGA AI Suite PCIe-based Design Example User Guide Archives B. FPGA AI Suite PCIe-based Design Example User Guide Document Revision History
4. Building the FPGA AI Suite Runtime x
4.1. CMake Targets 4.2. Build Options
5. Running the Design Example Demonstration Applications x
5.1. Exporting Trained Graphs from Source Frameworks 5.2. Compiling Exported Graphs Through the FPGA AI Suite 5.3. Compiling the PCIe* -based Example Design 5.4. Programming the FPGA Device ( Agilex™ 7) 5.5. Performing Accelerated Inference with the dla_benchmark Application 5.6. Running the Ported OpenVINO™ Demonstration Applications
5.5. Performing Accelerated Inference with the dla_benchmark Application x
5.5.1. Inference on Image Classification Graphs 5.5.2. Inference on Object Detection Graphs 5.5.3. Additional dla_benchmark Options 5.5.4. The dla_benchmark Performance Metrics
5.5.2. Inference on Object Detection Graphs x
5.5.2.1. The mAP and COCO AP Metrics 5.5.2.2. Specifying Ground Truth 5.5.2.3. Example of Inference on Object Detection Graphs
5.5.4. The dla_benchmark Performance Metrics x
5.5.4.1. Interpreting System Throughput and Latency Metrics
5.6. Running the Ported OpenVINO™ Demonstration Applications x
5.6.1. Example Running the Object Detection Demonstration Application
6. Design Example Components x
6.1. Build Script 6.2. Example Architecture Bitstream Files 6.3. Software Components 6.4. Software Interface to the BSP
6.1. Build Script x
6.1.1. Build Script Options 6.1.2. Script Flow
6.3. Software Components x
6.3.1. OpenVINO™ FPGA Runtime Plugin 6.3.2. FPGA AI Suite Runtime 6.3.3. BSP Driver
7. Design Example System Architecture for the Agilex™ 7 FPGA x
7.1. System Overview 7.2. Hardware
7.2. Hardware x
7.2.1. PLL Adjustment
1. FPGA AI Suite PCIe-based Design Example User Guide
2. About the PCIe* -based Design Example
3. Getting Started with the FPGA AI Suite PCIe* -based Design Example
4. Building the FPGA AI Suite Runtime
5. Running the Design Example Demonstration Applications
6. Design Example Components
7. Design Example System Architecture for the Agilex™ 7 FPGA
A. FPGA AI Suite PCIe-based Design Example User Guide Archives
B. FPGA AI Suite PCIe-based Design Example User Guide Document Revision History

5.6.1. Example Running the Object Detection Demonstration Application

You must download the following items:

  • yolo-v3-tf from the OpenVINO™ Model Downloader. The command should look similar to the following command:
    python3 <path_to_installation>/open_model_zoo/omz_downloader \
   --name yolo-v3-tf \
   --output_dir <download_dir>
    From the downloaded model, generate the .bin/.xml files: 
    python3 <path_to_installation>/open_model_zoo/omz_converter \
   --name yolo-v3-tf \
   --download_dir <download_dir> \
   --output_dir <output_dir> \
   --mo <path_to_installation>/model_optimizer/mo.py

    Model Optimizer generates an FP32 version and an FP16 version. Use the FP32 version.

To run the object detection demonstration application,
  1. Ensure that demonstration applications have been built with the following command:
    build_runtime.sh -target_de10_agilex -build-demo
  2. Ensure that the FPGA has been configured with the Generic bitstream.
  3. Run the following command:
    ./runtime/build_Release/object_detection_demo/object_detection_demo \
   -d HETERO:FPGA,CPU \
   -i <path_to_video>/input_video.mp4 \
   -m <path_to_model>/yolo_v3.xml \
   -arch_file=$COREDLA_ROOT/example_architectures/AGX7_Generic.arch \
   -plugins $COREDLA_ROOT/runtime/plugins.xml \  
   -t 0.65 \
   -at yolo
Tip: High-resolution video input, such as when using HD camera as input, imposes considerable decoding overhead on the inference engine that can potentially lead to reduced system throughput. Use the the -input_resolution=<width>x<height> option that is included in the demonstration application to adjust the input resolution to a level that balances video quality with system performance.
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