Intel® FPGA AI Suite: SoC Design Example User Guide

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Date 9/06/2023
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Document Table of Contents
Document Table of Contents x
1. Intel® FPGA AI Suite SoC Design Example User Guide 2. About the SoC Design Example 3. Intel® FPGA AI Suite SoC Design Example Quick Start Tutorial 4. Intel® FPGA AI Suite SoC Design Example Run Process 5. Intel® FPGA AI Suite SoC Design Example Build Process 6. Intel® FPGA AI Suite SoC Design Example Intel® Quartus® Prime System Architecture 7. Intel® FPGA AI Suite Soc Design Example Software Components 8. Streaming-to-Memory (S2M) Streaming Demonstration A. Intel® FPGA AI Suite SoC Design Example User Guide Archives B. Intel® FPGA AI Suite SoC Design Example User Guide Document Revision History
2. About the SoC Design Example x
2.1. Intel® FPGA AI Suite SoC Design Example Prerequisites
3. Intel® 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 the Intel® Arria® 10 SX SoC FPGA Development Kit for the Intel® FPGA AI Suite SoC Design Example 3.6. Adding Compiled Graphs (AOT files) to the SD Card 3.7. 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 the Intel® Arria® 10 SX SoC FPGA Development Kit for the Intel® FPGA AI Suite SoC Design Example x
3.5.1. Confirming Intel® Arria® 10 SX SoC FPGA Development Kit Board Settings 3.5.2. Connect the Intel® Arria® 10 SX SoC FPGA Development Kit to the Host Development System 3.5.3. Configuring the Intel® Arria® 10 SX SoC FPGA Development Kit UART Connection 3.5.4. Determining the Intel® Arria® 10 SX SoC FPGA Development Kit IP Address
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
3.7. Running the Demonstration Applications x
3.7.1. Running the M2M Mode Demonstration Application 3.7.2. Running the S2M Mode Demonstration Application 3.7.3. Troubleshooting the Demonstration Applications
4. Intel® FPGA AI Suite SoC Design Example Run Process x
4.1. Exporting Trained Graphs from Source Frameworks 4.2. Compiling Exported Graphs Through the Intel® FPGA AI Suite
5. Intel® FPGA AI Suite SoC Design Example Build Process x
5.1. Building the Intel® Quartus® Prime Project 5.2. Building the Bootable SD Card Image (.wic)
5.1. Building the Intel® Quartus® Prime Project x
5.1.1. Intel® Quartus® Prime Build Flow 5.1.2. Build Script Options 5.1.3. Build Directory
5.1.1. Intel® 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. Intel® FPGA AI Suite SoC Design Example Intel® Quartus® Prime System Architecture x
6.1. Intel® 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 Intel® FPGA AI Suite 6.3.2. Nios® V Subsystem 6.3.3. Streaming System Operation 6.3.4. Resolving Input Rate Mismatches Between the Intel® 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. Intel® FPGA AI Suite Soc Design Example Software Components x
7.1. Yocto Build and Runtime Linux Environment 7.2. Intel® 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_%.bbapend 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. Intel® FPGA AI Suite SoC Design Example User Guide
2. About the SoC Design Example
3. Intel® FPGA AI Suite SoC Design Example Quick Start Tutorial
4. Intel® FPGA AI Suite SoC Design Example Run Process
5. Intel® FPGA AI Suite SoC Design Example Build Process
6. Intel® FPGA AI Suite SoC Design Example Intel® Quartus® Prime System Architecture
7. Intel® FPGA AI Suite Soc Design Example Software Components
8. Streaming-to-Memory (S2M) Streaming Demonstration
A. Intel® FPGA AI Suite SoC Design Example User Guide Archives
B. Intel® FPGA AI Suite SoC Design Example User Guide Document Revision History

6.3.5. The Layout Transform IP as an Application-Specific Block

The layout transformation IP in the S2M design is provided as RTL source as an example layout transformation within a video inferencing application.

The flexibility of the Intel® FPGA AI Suite and the scope of projects it can support means that a layout transformation IP cannot serve all inference applications.

Each target application typically requires its own layout transformation module to be designed. System architectures need to budget for this design effort within their project.

Input data to the Intel® FPGA AI Suite IP must be formatted in memory so that the data matches the structure of the IP PE array and uses FP16 values.

The structure of the PE array is defined by the architecture file and the c_vector parameter setting describes the number of layers required for the input buffer. Typical c_vector values are 8, 16, and 32.

When considering streaming data, the c_vector can be understood in comparison to the number of input channels of data that is present. For example, video has red, green, and blue channels that make up each pixel color. The following diagram shows how the video channels map to the input data stream required by the Intel® FPGA AI Suite IP.
Figure 9. Input Data Steam Mapping

The S2M design demonstrates an example of video streaming. Pixel data is sent through the layout-transform as RGB pixels, where each color is considered an input channel of data.

As the input data comprise only three channels of input data, the input data must be padded with zeros for any unused channels. The following diagram shows an example of two architectures, one with c_vector value of 8 and another with c_vector value of 16.

In the first example where c_vector is set to 8, the first pixel of RGB is placed on the input stream filling the first 3 channels, but there are 5 more channels remaining that must be initialized. These are filled with zero (represented by the white squares). This padded stream is then fed into the Nios® subsystem.

This example layout transform does not support input folding. Input folding is an input preprocessing step that reduces the amount of zero padding in the c_vector. This folding then enables more efficient use of the dot product engine in the Intel® FPGA AI Suite IP. The efficiency gains can be significant depending on the graph and C_VEC. For more details, refer to "Input Folding" in Intel® FPGA AI Suite IP Reference Manual .


Section Content
Layout Transform Considerations
Layout Transform IP Register Map
Layout Transform Configuration Options

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