FPGA AI Suite: Getting Started Guide

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ID 768970
Date 3/29/2024
Public
Document Table of Contents
Document Table of Contents x
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 A. FPGA AI Suite Getting Started Guide Archives B. FPGA AI Suite Getting Started Guide Document Revision History
3. FPGA AI Suite Installation Overview x
3.1. FPGA AI Suite Directory Structure
4. Installing the FPGA AI Suite Compiler and IP Generation Tools x
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
4.3. Installing the FPGA AI Suite with System Package Management Tools x
4.3.1. Installing the FPGA AI Suite Into an Alternative Location
5. Installing the FPGA AI Suite PCIe-Based Design Example Prerequisites x
5.1. PCIe-based Design Example Hardware Prerequisites 5.2. PCIe-based Design Example Operating System Prerequisites 5.3. PCIe-based Design Example Software Prerequisites
5.3. PCIe-based Design Example Software Prerequisites x
5.3.1. Additional Software Prerequisites for the PCIe-based Design Example for Agilex™ 7 Devices 5.3.2. Additional Software Prerequisites for the PCIe-Based Design Example for Arria® 10 Devices
6. FPGA AI Suite Quick Start Tutorial x
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
6.12. 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
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
A. FPGA AI Suite Getting Started Guide Archives
B. FPGA AI Suite Getting Started Guide Document Revision History

6.4. Running the Graph Compiler

The FPGA AI Suite compiler (dla_compiler tool) can estimate the performance of a graph and IP architecture combination. The tool can also produce a compiled network that the runtime uses in AOT (ahead-of-time) mode to control inference on the IP.

The following commands generate an AOT file for Agilex™ 7 devices: 
cd $COREDLA_WORK/demo/models/public/resnet-50-tf/FP32
            dla_compiler \
            --march $COREDLA_ROOT/example_architectures/AGX7_Performance.arch \
            --network-file ./resnet-50-tf.xml \
            --foutput-format=open_vino_hetero \
            --o $COREDLA_WORK/demo/RN50_Performance_b1.bin \
            --batch-size=1 \
            --fanalyze-performance
The following commands generate an AOT file for Arria® 10 devices: 
cd $COREDLA_WORK/demo/models/public/resnet-50-tf/FP32
dla_compiler \
	--march $COREDLA_ROOT/example_architectures/A10_Performance.arch \
	--network-file ./resnet-50-tf.xml \
	--foutput-format=open_vino_hetero \
	--o $COREDLA_WORK/demo/RN50_Performance_b1.bin \
	--batch-size=1 \
	--fanalyze-performance
These command specify the following input files:
  • The .arch file specifies the configuration of the IP architecture (configuration parameters such as large, small, and which activations to use).
  • The .xml file (along with the .bin) describes the network.
  • The .bin file stores certain parameters along with the model weights.
The FPGA AI Suite compiler uses these files to analyze the performance estimate.

The output file RN50_Performance_b1.bin is the compiled network and contains the instructions and graph weights necessary to control the FPGA AI Suite IP on the FPGA device. This output file is specific to the .arch file and the graph .xml file.

The compiler dot graph shows which layers run on the FPGA and which layers run on the CPU. Using the Graphviz package, the dot file format can be converted into an SVG graphics file named ResNet-50.svg that shows each layer. You can view SVG graphics in most web browsers. Use the following command to convert the dot graph file into an SVG file: 
dot -Tsvg hetero_subgraphs_resnet-50-tf.dot -o ResNet-50.svg
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