FPGA AI Suite: Getting Started Guide

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ID 768970
Date 7/31/2024
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 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
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
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 6.13. Performing Inference Without an FPGA Board
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
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

7. Running the Hostless Memoryless Design Example

The FPGA AI Suite provides a design example to demonstrate hostless and memoryless operation of the FPGA AI Suite IP. Graph filters, bias, and FPGA AI Suite IP configurations are stored in on-chip memory on the FPGA device instead of DDR memory on the board.

For more details about memoryless operation, refer to Memoryless Operation in the FPGA AI Suite IP Reference Manual .

Hardware Requirements

This design example requires the following hardware:

Software Requirements

This design example requires the following software:
  • FPGA AI Suite
  • Quartus® Prime Programmer (either standalone or as part of Quartus® Prime Design Suite).
  • Quartus® Prime System Console (either standalone or as part of Quartus® Prime Design Suite).

Procedure

To run the hostless memoryless design example:
  1. Generate the parameter ROMs as .mif files by compiling the design example with the following command:
    dlac \
  --batch-size=1 \
  --network-file <path/to/graph> \
  --march <path/to/memoryless/arch> \
  --foutput-format=open_vino_hetero \
  –o <compiler output .bin file name>  \
  --fplugin HETERO:FPGA \
  --enable-early-access

    The .mif files are created in the current directory.

    For details about creating the .mif files required for memoryless operation, refer to "Generating Artifacts for Memoryless Operation" in the FPGA AI Suite Compiler Reference Manual .

  2. Build the example design with the following command:
    dla_build_example_design.py \
  -n 1 \
  --arch=<path/to/memoryless/arch> \
  --build --build-dir=<path/to/build/dir> \
  --example-design-id=0_STREAMING \
  --seed=1 \
  --parameter-rom-dir <path/to/mif/files>

    Building the example design creates the bitstream needed to program the FPGA device.

    For more information about the dla_build_example_design command, refer to "Build Script" in FPGA AI Suite PCIe-based Design Example User Guide .

  3. Program the FPGA device with the Quartus® Prime Programmer.

    The bitstream used to program the device is <path/to/build/dir>/hw/output_files/top.sof.

    Program the FPGA device with the following command: 
    quartus_pgm -c 1 -m jtag -o "p;top.sof@1"

    For more information about the Quartus® Prime Programmer, refer to Quartus® Prime Pro Edition User Guide: Programmer .

  4. Use the Quartus® Prime System Console to run inference on the example design.

    Because this example design is hostless, operations that typically come from the host are performed through Quartus® Prime System Console instead. For more information about the Quartus® Prime System Console, refer to "Analyzing and Debugging Designs with System Console" in Quartus® Prime Pro Edition User Guide: Debug Tools .

    Use the System Console to complete the following steps:
    1. Store input features in the FPGA on-chip memory.
    2. Prime the FPGA AI Suite IP registers for inference.
    3. Configure an ingress Modular Scatter-Gather DMA (mSGDMA) core to read the input features from on-chip memory and stream data into the FPGA AI Suite IP.
    4. Configure an egress mSGDMA core to stream data from the FPGA AI Suite IP into on-chip memory.
    5. Read the inference results from on-chip memory.

    The design example provides a System Console script to automate these operations for you. You can find the script in the $CORDLA_ROOT/runtime/stream/ed0_streaming_example folder.

    If you want to use the provided script for you own design, ensure that you review the information about the mSGDMA core, refer to "Modular Scatter-Gather DMA Core" in Embedded Peripherals IP User Guide to understand how to extend the script for your design.

    To use the design example System Console script:
    1. Copy a raw img.bin file to the $COREDLA_ROOT/runtime/streaming/ed0_streaming_example directory. The System Console script uses the img.bin file as input to the model.
    2. Run the following command:
      system-console –-script=system_console_script.tcl

      The design example System Console script generates a file called output.bin that contains the raw inference results.

  5. (Optional) Postprocess the raw inference output for readability.

    The design example provides the $COREDLA_ROOT/bin/streaming_post_processing.py script to clean the raw output.bin file. This script removes some invalid bytes and stores an FP16 formatted result_hw.txt file for readability.

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