FPGA AI Suite: SoC Design Example User Guide

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ID 768979
Date 7/31/2024
Public
Document Table of Contents
Document Table of Contents x
1. FPGA AI Suite SoC Design Example User Guide 2. About the SoC Design Example 3. FPGA AI Suite SoC Design Example Quick Start Tutorial 4. FPGA AI Suite SoC Design Example Run Process 5. FPGA AI Suite SoC Design Example Build Process 6. FPGA AI Suite SoC Design Example Quartus® Prime System Architecture 7. FPGA AI Suite Soc Design Example Software Components 8. Streaming-to-Memory (S2M) Streaming Demonstration A. FPGA AI Suite SoC Design Example User Guide Archives B. FPGA AI Suite SoC Design Example User Guide Document Revision History
2. About the SoC Design Example x
2.1. FPGA AI Suite SoC Design Example Prerequisites
2.1. FPGA AI Suite SoC Design Example Prerequisites x
2.1.1. Agilex™ 7 FPGA I-Series Transceiver-SoC Development Kit Hardware Requirements
3. FPGA AI Suite SoC Design Example Quick Start Tutorial x
3.1. Initial Setup 3.2. Initializing a Work Directory 3.3. (Optional) Create an SD Card Image (.wic) 3.4. Writing the SD Card Image (.wic) to an SD Card 3.5. Preparing SoC FPGA Development Kits for the FPGA AI Suite SoC Design Example 3.6. Adding Compiled Graphs (AOT files) to the SD Card 3.7. Verifying FPGA Device Drivers 3.8. Running the Demonstration Applications
3.3. (Optional) Create an SD Card Image (.wic) x
3.3.1. Installing Prerequisite Software for Building an SD Card Image 3.3.2. Building the FPGA Bitstreams 3.3.3. Installing HPS Disk Image Build Prerequisites 3.3.4. (Optional) Downloading the ImageNet Categories 3.3.5. Building the SD Card Image
3.5. Preparing SoC FPGA Development Kits for the FPGA AI Suite SoC Design Example x
3.5.1. Preparing the Agilex™ 7 FPGA I-Series Transceiver-SoC Development Kit 3.5.2. Preparing the Arria® 10 SX SoC FPGA Development Kit 3.5.3. Configuring the SoC FPGA Development Kit UART Connection 3.5.4. Determining the SoC FPGA Development Kit IP Address
3.5.1. Preparing the Agilex™ 7 FPGA I-Series Transceiver-SoC Development Kit x
3.5.1.1. Confirming Agilex™ 7 FPGA I-Series Transceiver-SoC Development Kit Board Set Up 3.5.1.2. Programming the Agilex™ 7FPGA Device with the JTAG Indirect Configuration (.jic) File 3.5.1.3. Connecting the Agilex™ 7 FPGA I-Series Transceiver-SoC Development Kit to the Host Development System
3.5.2. Preparing the Arria® 10 SX SoC FPGA Development Kit x
3.5.2.1. Confirming Arria® 10 SX SoC FPGA Development Kit Board Settings 3.5.2.2. Connecting the Arria® 10 SX SoC FPGA Development Kit to the Host Development System
3.6. Adding Compiled Graphs (AOT files) to the SD Card x
3.6.1. Preparing OpenVINO™ Model Zoo 3.6.2. Preparing a Model 3.6.3. Compiling the Graphs 3.6.4. Copying the Compiled Graphs to the SD card
3.8. Running the Demonstration Applications x
3.8.1. Running the M2M Mode Demonstration Application 3.8.2. Running the S2M Mode Demonstration Application 3.8.3. Troubleshooting the Demonstration Applications
4. FPGA AI Suite SoC Design Example Run Process x
4.1. Exporting Trained Graphs from Source Frameworks 4.2. Compiling Exported Graphs Through the FPGA AI Suite
5. FPGA AI Suite SoC Design Example Build Process x
5.1. Building the Quartus® Prime Project 5.2. Building the Bootable SD Card Image (.wic)
5.1. Building the Quartus® Prime Project x
5.1.1. Quartus® Prime Build Flow 5.1.2. Build Script Options 5.1.3. Build Directory
5.1.1. Quartus® Prime Build Flow x
5.1.1.1. Build Synchronization of FPGA with Software
5.1.3. Build Directory x
5.1.3.1. The create_project.bash Script 5.1.3.2. The generate_sof.bash Script 5.1.3.3. The generate_rbf.bash Script 5.1.3.4. The build_stream_controller.sh Script
6. FPGA AI Suite SoC Design Example Quartus® Prime System Architecture x
6.1. FPGA AI Suite SoC Design Example Inference Sequence Overview 6.2. Memory-to-Memory (M2M) Variant Design 6.3. Streaming-to-Memory (S2M) Variant Design 6.4. Top Level 6.5. The SoC Design Example Platform Designer System 6.6. Fabric EMIF Design Component 6.7. PLL Configuration
6.2. Memory-to-Memory (M2M) Variant Design x
6.2.1. The mSGDMA Intel FPGA IP 6.2.2. RAM considerations
6.3. Streaming-to-Memory (S2M) Variant Design x
6.3.1. Streaming Enablement for FPGA AI Suite 6.3.2. Nios® V Subsystem 6.3.3. Streaming System Operation 6.3.4. Resolving Input Rate Mismatches Between the FPGA AI Suite IP and the Streaming Input 6.3.5. The Layout Transform IP as an Application-Specific Block
6.3.3. Streaming System Operation x
6.3.3.1. Streaming System Buffer Management 6.3.3.2. Streaming System Inference Job Management
6.3.5. The Layout Transform IP as an Application-Specific Block x
6.3.5.1. Layout Transform Considerations 6.3.5.2. Layout Transform IP Register Map 6.3.5.3. Layout Transform Configuration Options
6.4. Top Level x
6.4.1. Clock Domains
6.5. The SoC Design Example Platform Designer System x
6.5.1. The dla_0 Platform Designer Layer (dla.qsys) 6.5.2. The hps_0 Platform Designer Layer (hps.qys)
7. FPGA AI Suite Soc Design Example Software Components x
7.1. Yocto Build and Runtime Linux Environment 7.2. FPGA AI Suite Runtime Plugin 7.3. Runtime Interaction with the MMD Layer 7.4. MMD Layer Hardware Interaction Library
7.1. Yocto Build and Runtime Linux Environment x
7.1.1. Yocto Recipe: recipes-core/images/coredla-image.bb 7.1.2. Yocto Recipe: recipes-bsp/u-boot/u-boot-socfpga_%.bbappend 7.1.3. Yocto Recipe: recipes-drivers/msgdma-userio/msgdma-userio.bb 7.1.4. Yocto Recipe: recipes-drivers/uio-devices/uio-devices.bb 7.1.5. Yocto Recipe: recipes-kernel/linux/linux-socfpga-lts_5.15.bbappend 7.1.6. Yocto Recipe: wic
7.4. MMD Layer Hardware Interaction Library x
7.4.1. MMD Layer Hardware Interaction Library Class mmd_device 7.4.2. MMD Layer Hardware Interaction Library Class uio_device 7.4.3. MMD Layer Hardware Interaction Library Class dma_device
8. Streaming-to-Memory (S2M) Streaming Demonstration x
8.1. Nios® Subsystem 8.2. Building the Stream Controller Module 8.3. Building the Streaming Demonstration Applications 8.4. Running the Streaming Demonstration
8.1. Nios® Subsystem x
8.1.1. Stream Controller Communication Protocol 8.1.2. Buffer Flow in Streaming Mode using Nios® V Software Scheduler
8.1.2. Buffer Flow in Streaming Mode using Nios® V Software Scheduler x
8.1.2.1. Review of M2M mode Preprocessing Steps Inference Execution Steps Postprocessing Steps 8.1.2.2. External Streaming Mode Buffer Flow 8.1.2.3. Nios® V Stream Controller State Machine Buffer Flow
8.4. Running the Streaming Demonstration x
8.4.1. The streaming_inference_app Application 8.4.2. The image_streaming_app Application
1. FPGA AI Suite SoC Design Example User Guide
2. About the SoC Design Example
3. FPGA AI Suite SoC Design Example Quick Start Tutorial
4. FPGA AI Suite SoC Design Example Run Process
5. FPGA AI Suite SoC Design Example Build Process
6. FPGA AI Suite SoC Design Example Quartus® Prime System Architecture
7. FPGA AI Suite Soc Design Example Software Components
8. Streaming-to-Memory (S2M) Streaming Demonstration
A. FPGA AI Suite SoC Design Example User Guide Archives
B. FPGA AI Suite SoC Design Example User Guide Document Revision History

8.1.2.1. Review of M2M mode

To explain how the buffers are managed in streaming mode, it can help to review the existing flow for M2M mode.

The inference application loads source images from .bmp files into memory allocated from its heap. These buffers are 224x224x3 uint8_t samples (150528 bytes). During the load, the BGR channels are rearranged from interleaved channels into planes.

OpenVINO™ inference requests are created by the application using the inference engine. These inference requests allocate buffers in the on-board EMIF memory. The size of each of these buffers is the size of the input buffer plus the size of the output buffer. The input buffer size depends FPGA AI Suite IP parameters (specified in the .arch file) for which the graph was compiled.

The BGR planar image buffers are attached as input blobs to these OpenVINO™ inference requests, which are then scheduled for execution.

Preprocessing Steps

In M2M mode, the preprocessing steps are performed in software.

  • The samples are converted to 32-bit floating point, and the mean G, B and R values of the imagenet dataset are subtracted from each sample accordingly.
  • The samples are converted to 16-bit floating point.
  • A layout transform then maps these samples into a larger buffer which has padding, in the layout expected by the FPGA AI Suite.

Inference Execution Steps

  • The transformed image is written directly to board memory at its allocated address.
  • The FPGA AI Suite IP CSR registers are programmed to schedule the inference.
  • The FPGA AI Suite OpenVINO™ plugin monitors the completion count register (located on the FPGA AI Suite IP), either by polling or receiving an interrupt, and waits until the count increments.
  • The results buffer (2048 bytes) is read directly from the EMIF on the board to HPS memory.

Postprocessing Steps

  • The samples in the results buffer (1001 16-bit floating point values) are converted to 32-bit floating point.
  • The inference application receives these buffers, sorts them, and collects the top five results.
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