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 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

6.1. FPGA AI Suite SoC Design Example Inference Sequence Overview

The FPGA AI Suite IP works with system memory. To communicate with the system memory, the FPGA AI Suite has its own multichannel DMA engine.

This DMA engine pulls input commands and data from the system memory. It then writes the output data back to this memory for a Host CPU to collect.

When running inferences, the FPGA AI Suite continually reads and writes intermediate buffers to and from the system memory. The allocation of all the buffer addresses is done by the FPGA AI Suite runtime software library.

Running an inference requires minimal interaction between the host CPU and the IP Registers.

The system memory must be primed with all necessary buffers before starting a machine learning inference operation. These buffers are setup by the FPGA AI Suite runtime library and application that runs on the host CPU.

After the setup is complete, the host CPU pushes a job into the IP registers.

The FPGA AI Suite IP now performs a single inference. The job-queue registers in the IP are FIFO based, and the host application can store multiple jobs in the system memory and then prime multiple jobs inside the IP. Each job stores results in system memory and results in a CPU interrupt request.

For each inference operation in the M2M model, the host CPU (HPS) must perform an extensive data transfer from host (HPS) DDR memory to the external DDR memory that is allocated to the FPGA AI Suite IP. As this task has not been FPGA-accelerated in the design, the host operating system and FPGA AI Suite runtime library must manually transfer the data. This step consumes significant CPU resources. The M2M design uses a DMA engine to help with the data transfer from HPS DDR to the allocated DDR memory.

The FPGA AI Suite inference application and library software are responsible for keeping the FPGA AI Suite IP primed with new input data and responsible for consuming the results.

Figure 3.  FPGA AI Suite SoC Design Example Inference Sequence Overview

For a detailed overview of the FPGA AI Suite IP inference sequence, refer to the FPGA AI Suite IP Reference Manual .

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