Intel® FPGA AI Suite: Getting Started Guide

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ID 768970
Date 4/05/2023
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Document Table of Contents
Document Table of Contents x
1. Intel® FPGA AI Suite Getting Started Guide 2. About the Intel® FPGA AI Suite 3. Installing the Intel® FPGA AI Suite 4. Installing the Intel® FPGA AI Suite PCIe-Based Design Example Prerequisites 5. Installing the Intel FPGA AI Suite Compiler and IP Generation Tools 6. Intel® FPGA AI Suite Quick Start Tutorial A. Installation Notes for Other Operating Systems B. Intel® FPGA AI Suite Getting Started Guide Archives C. Intel® FPGA AI Suite Getting Started Guide Document Revision History
3. Installing the Intel® FPGA AI Suite x
3.1. Intel® FPGA AI Suite Directory Structure
4. Installing the Intel® FPGA AI Suite PCIe-Based Design Example Prerequisites x
4.1. PCIe-based Design Example Hardware Prerequisites 4.2. PCIe-based Design Example Operating System Prerequisites 4.3. PCIe-based Design Example Software Prerequisites
4.3. PCIe-based Design Example Software Prerequisites x
4.3.1. Additional Software Prerequisites for the PCIe-based Design Example for Intel Agilex® 7 Devices 4.3.2. Additional Software Prerequisites for the PCIe-Based Design Example for Intel® Arria® 10 Devices
5. Installing the Intel FPGA AI Suite Compiler and IP Generation Tools x
5.1. Supported FPGA Families 5.2. Operating System Prerequisites 5.3. Software Prerequisites 5.4. Installing the Intel® FPGA AI Suite Using System Package Management Tools 5.5. Installing the Intel® FPGA AI Suite Into an Alternative Location
5.3. Software Prerequisites x
5.3.1. OpenVINO™ Toolkit 5.3.2. Intel® Quartus® Prime Pro Edition Software
6. Intel® FPGA AI Suite Quick Start Tutorial x
6.1. Creating a Working Directory 6.2. Preparing OpenVINO™ Model Zoo and Model Optimizer 6.3. Preparing a Model 6.4. Running the Graph Compiler 6.5. Preparing an Image Set 6.6. Programming the FPGA Device 6.7. Performing Inference on the PCIe-Based Example Design 6.8. Building an FPGA Bitstream for the PCIe Example Design 6.9. Building the Example FPGA Bitstreams 6.10. Preparing a ResNet50 v1 Model 6.11. Performing Inference on the Inflated 3D (I3D) Graph 6.12. Performing Inference on YOLOv3 and Calculating Accuracy Metrics
6.12. Performing Inference on YOLOv3 and Calculating Accuracy Metrics x
6.12.1. Preparing a YOLOv3 Model 6.12.2. Preparing a COCO Validation Dataset and Annotations 6.12.3. Inference on YOLOv3 and Calculating Accuracy Scores
A. Installation Notes for Other Operating Systems x
A.1. CentOS* 7 Installation Notes
1. Intel® FPGA AI Suite Getting Started Guide
2. About the Intel® FPGA AI Suite
3. Installing the Intel® FPGA AI Suite
4. Installing the Intel® FPGA AI Suite PCIe-Based Design Example Prerequisites
5. Installing the Intel FPGA AI Suite Compiler and IP Generation Tools
6. Intel® FPGA AI Suite Quick Start Tutorial
A. Installation Notes for Other Operating Systems
B. Intel® FPGA AI Suite Getting Started Guide Archives
C. Intel® FPGA AI Suite Getting Started Guide Document Revision History

6.12.3. Inference on YOLOv3 and Calculating Accuracy Scores

To run inference on YOLOv3 and calculate the mAP and COCO AP scores, run the following commands: 
cd $COREDLA_WORK/runtime
./build_Release/dla_benchmark/dla_benchmark \ 
   -b=1 \ 
   -niter=5000 \ 
   -m $COREDLA_WORK/demo/models/public/yolo-v3-tf/FP32/yolo-v3-tf.xml \ 
   -d=HETERO:FPGA,CPU \ 
   -i=./coco-images/val2017 \ 
   -plugins_xml_file=../plugins.xml \ 
   -arch_file=$COREDLA_ROOT/example_architectures/A10_Performance.arch \ 
   -yolo_version=yolo-v3-tf \ 
   -api=async \ 
   -groundtruth_loc=./coco-images/groundtruth \ 
   -nireq=4 \ 
   -enable_object_detection_ap \ 
   -perf_est \ 
   -bgr

The dla_benchmark command prints the mAP and COCO AP scores and saves a text file called ap_report.txt that contains the scores in the current working directory.

To enable the accuracy checking routine for object detection graphs such as YOLOv3, use the -enable_object_detection_ap=1 option of the dla_benchmark command. This flag directs the dla_benchmark command to calculate the mAP and COCO AP for object detection graphs.

Also, specify the version of the YOLO graph that you provide to the dla_benchmark command with the -yolo_version= yolo-v3-tf option.

The input images folder is specified with -i=./coco-images and the ground truth annotations is specified with -groundtruth_loc=./groundtruth. If you chose to save the images or the ground truth annotations to a location other than the ones specified in this tutorial, update these parameters to point to the correct location.

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