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1. FPGA AI Suite Getting Started Guide
2. FPGA AI Suite Components
3. FPGA AI Suite Installation Overview
4. Installing the FPGA AI Suite Compiler and IP Generation Tools
5. Installing the FPGA AI Suite PCIe-Based Design Example Prerequisites
6. FPGA AI Suite Quick Start Tutorial
7. Running the Hostless Memoryless Design Example
A. FPGA AI Suite Getting Started Guide Archives
B. FPGA AI Suite Getting Started Guide Document Revision History
4.1. Supported FPGA Families
4.2. Operating System Prerequisites
4.3. Installing the FPGA AI Suite with System Package Management Tools
4.4. Installing OpenVINO™ Toolkit
4.5. Installing Quartus® Prime Pro Edition Software
4.6. Setting Required Environment Variables
4.7. Installing Intel® Threading Building Blocks (TBB)
4.8. Finalizing Your FPGA AI Suite Installation
6.1. Creating a Working Directory
6.2. Preparing OpenVINO™ Model Zoo
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
6.13. Performing Inference Without an FPGA Board
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6.12. Performing Inference on YOLOv3 and Calculating Accuracy Metrics
This tutorial targets Agilex™ 7 devices and assumes that you have the following prerequisites completed:
- You have a copy of of OpenVINO™ Model Zoo 2023.3 in your $COREDLA_ROOT/demo/open_model_zoo/ directory.
- You have completed the steps in Preparing OpenVINO Model Zoo.
- You have programmed the Agilex™ 7 FPGA device with a bitstream that corresponds to AGX7_Performance.arch. For example instructions, refer to Building an FPGA Bitstream for the PCIe Example Design.
The steps in the sections that follow guide you through preparing a YOLOv3 model for inference, preparing a COCO validation dataset and annotations, as well as calculating mAP and COCO AP metrics.