FPGA AI Suite: PCIe-based Design Example User Guide

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ID 768977
Date 12/16/2024
Public
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

5.5.2.3. Example of Inference on Object Detection Graphs

The example that follows makes the following assumptions:
  • The Model Optimizer IR graph.xml for either YOLOv3 or TinyYOLOv3 is in the current working directory.

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

  • The validation images downloaded from the COCO website are placed in the ./mscoco-images directory.
  • The JSON annotation file is downloaded and unzipped in the current directory.
To compute the accuracy scores on many images, you can usually increase the number of iterations using the flag -niter instead of a large batch size -b. The product of the batch size and the number of iterations should be less than or equal to the number of images that you provide. 
cd $COREDLA_ROOT/runtime/build_Release
python ./convert_annotations.py ./instances_val2017.json \
   ./groundtruth
./dla_benchmark/dla_benchmark \
   -b=1 \
   -niter=5000 \
   -m=./graph.xml \
   -d=HETERO:FPGA,CPU \
   -i=./mscoco-images \
   -plugins=./plugins.xml \
   -arch_file=../../example_architectures/AGX7_Performance.arch \
   -yolo_version=yolo-v3-tf \
   -api=async \
   -groundtruth_loc=./groundtruth \
   -nireq=8 \
   -enable_object_detection_ap \
   -perf_est \
   -bgr
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