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 Inference Execution Steps Postprocessing Steps 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

8.1.2.2. External Streaming Mode Buffer Flow

External streaming mode is enabled in the inference application by setting the configuration value DLIA_CONFIG_KEY(EXTERNAL_STREAMING) to CONFIG_VALUE(YES) in the OpenVINO™ FPGA plugin.

In streaming mode, the inference application does not handle any of the input buffers. It still must create inference requests, which allocate the input and output buffers in the EMIF memory as before, but no input blobs are attached to the inference requests.

When the inference request is executed, there are no preprocessing steps required, since they do not have any input blobs.

Inference Execution Steps

  • Instead of writing a source buffer directly to its allocated address, a ScheduleItem command is sent to the Nios® V stream controller which contains details of the input buffer EMIF address.
  • The FPGA AI Suite IP CSR registers are not programmed by the plugin.
  • The plugin waits for the completion count register to increment as before.
  • The results buffer is read directly from the board as before.

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.
  • The same inference request is rescheduled with the inference engine.
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