Integrating AI Engines into Custom FPGA Designs: A Step-By-Step Guide

Integrating AI Engines into Custom FPGA Designs: A Step-By-Step Guide

Table of Contents

1. Introduction
2. The Need for Adding AI Engines in FPGA Designs
3. Understanding the Device Architecture
4. Overview of the Vitus Design Tool
5. Requirements and Assumptions
6. Building a Custom Vivado IP Integrator Project
7. Exporting the Platform for Vitus
8. Creating an AI Engine Application Project
9. Compiling the AI Engine Code
10. Connecting the PL and AI Engines in Vitus
11. Packaging the Boot Image
12. Running the Design on Hardware
13. Probing the Interfaces with Integrated Logic Analyzers
14. Making Changes to the FPGA Design

Introduction

In this article, we will explore the process of integrating AI engines into custom FPGA (Field-Programmable Gate Array) designs. We will discuss the challenges faced by FPGA engineers when adding AI capabilities to their designs and provide step-by-step instructions on how to accomplish this task using the Vivado and Vitus design tools. By the end of this article, You will have a clear understanding of how to seamlessly integrate AI engines into your FPGA designs and evaluate their performance and functionality.

The Need for Adding AI Engines in FPGA Designs

Many FPGA engineers often find themselves in a situation where they need to incorporate AI engines into their existing designs. This requirement arises when they already have FPGA components like test pattern generators or interfaces in their design and want to evaluate the performance, power, efficiency, and functionality of AI engines. In order to achieve this, engineers must learn how to integrate AI engine DSP designs into their own custom PL (Programmable Logic) designs. In this article, we will guide you through the process of adding AI engines into FPGA designs using a Simplified and versatile workflow.

Understanding the Device Architecture

Before diving into the design process, it's important to have a clear understanding of the device architecture. FPGAs consist of adaptable engines, DSP (Digital Signal Processing) engines, ARM processors, platform management controllers, and AI engines. These components need to be properly programmed and configured to ensure seamless integration and functionality. Xilinx's Vitus design tool simplifies the management of this complexity by providing a unified platform that ties everything together and generates the boot image for deployment.

Overview of the Vitus Design Tool

Vitus is a powerful design tool developed by Xilinx to streamline the process of adding AI engines to FPGA designs. While it offers a comprehensive range of features and functionalities, this article will focus on a simplified usage of Vitus specifically for integrating AI engines with custom PL designs. We will guide you through the step-by-step process of using Vitus to Create an AI engine application project, compile the code, and generate the boot image. This simplified workflow allows engineers to quickly evaluate their custom AI and PL designs without the need for additional software or complex configurations.

Requirements and Assumptions

To make the most of this tutorial, we assume that you are already familiar with RTL (Register Transfer Level) engineering and have experience with the Xilinx Vivado design tool. We also expect that you have a basic understanding of AI engine design. While we will not cover the intricacies of designing AI engines in this article, we will provide you with the necessary steps to integrate them into your FPGA designs. Additionally, we assume that you are comfortable with Vivado and are using the Vivus tool specifically to tie the PL bitstream together with the AI engine binary image.

Building a Custom Vivado IP Integrator Project

To begin the design process, we will start by creating a new Vivado IP Integrator project. In this project, we will add the necessary IP components and configure their settings to match our requirements. We will add components such as the System IP (SIP) Wizard, AI engines, XE (Executable Entity) Nock, and custom RTL (Register Transfer Level) sources. These components will be interconnected using AXI (Advanced eXtensible Interface) and AXI Stream interfaces. Through this step, we will create a functional block design that serves as the foundation for integrating the AI engines.

Exporting the Platform for Vitus

Once the block design is complete, we need to export the platform from Vivado to Vitus. This platform contains all the necessary data and settings required by Vitus to properly integrate the AI engines into the PL design. We will Show you how to export the platform as an XSA (Extensible System Archive) file, which captures the block design, configurations, and sources. This file serves as the foundation for creating a new Vitus application project.

Creating an AI Engine Application Project

With the platform exported, we will now switch from Vivado to Vitus and create a new application project. In this project, we will specify the target platform and import the previously exported XSA file. By linking the platform and the AI engine graph code, we can define the connectivity between the PL and AI engines. This step allows Vitus to recognize and manipulate the AXI and AXI Stream interfaces within the design. We will guide you through this process, ensuring that the connections are correctly established.

Compiling the AI Engine Code

Before proceeding further, we need to compile the AI engine code and generate the boot image for deployment. This code is responsible for implementing the desired functionality of the AI engine in the PL design. Vitus provides the AI Engine Compiler tool to perform this compilation process. We will show you how to integrate the DSP Library, specify the top-level file, and configure the compiler settings. Once the compilation is complete, we will have a binary file that contains the final AI engine code.

Connecting the PL and AI Engines in Vitus

With the AI engine code compiled, we can now connect the PL and AI engines in Vitus. We will use the hardware link project within Vitus to specify the connections between the AI engine and the PLIO (Programmable Logic Input/Output) interfaces. By providing the necessary connectivity information, we can ensure that data flows seamlessly between the two components. We will guide you through the process of defining the connections and updating the project to reflect these configurations.

Packaging the Boot Image

Once the PL and AI engine connections are established, we can Package the boot image for deployment. This boot image includes the bitstream, the AI engine binary file, and other artifacts required for proper initialization and functionality. We will guide you through this process, showcasing the simplicity of packaging the boot image within Vitus. With the boot image ready, we can proceed to the final step of running the design on hardware.

Running the Design on Hardware

In this step, we will deploy the packaged boot image onto hardware to test the functionality and performance of the integrated AI engine and PL design. By inserting the SD card containing the boot image into the target FPGA board, we can power on the board and observe the boot process. We will walk you through the necessary steps to ensure a smooth deployment and provide Insight into the application of the design on the VCK 190 development board.

Probing the Interfaces with Integrated Logic Analyzers

To validate the behavior and performance of the AI engine and PL design, we will utilize integrated logic analyzers (ILAs). By probing the PLIO interfaces and AI engine interfaces, we can capture and analyze the input and output waveforms. This allows us to measure latency, performance, and functionality of the design in real-time. We will demonstrate how to configure the ILAs and view the captured waveforms using the hardware manager within Vivado.

Making Changes to the FPGA Design

In the final section of this article, we will address the process of making changes to the FPGA design after integrating the AI engines. We will guide you through modifying the block design in Vivado, saving and validating the changes, and updating the platform for Vitus. Once the platform is updated, we can recompile and relink the project in Vitus to reflect the latest changes. By following this workflow, you can easily iterate on your FPGA design and incorporate improvements or new features.

Conclusion

Integrating AI engines into FPGA designs may seem complex at first, but with the right tools and techniques, it can be accomplished efficiently and effectively. In this article, we have provided a comprehensive guide on how to seamlessly integrate AI engines into custom PL designs using the Vivado and Vitus design tools. By following the step-by-step instructions and leveraging the power of these tools, you can unlock the full potential of AI capabilities in your FPGA designs, allowing for enhanced performance, power efficiency, and functionality.