Fpga Project: Cnn Accelerator For Digit Recognition

From fundamental to the full deployment of a CNN Accelerator on Zynq FPGA

What you'll learn

Understand the fundamentals of CNN

Understand the Python model of CNN

Understand the RTL design of CNN Accelerator

Integrate the CNN Accelerator with an ARM processor on a Zynq FPGA

Develop a web-based GUI to interact with the CNN accelerator

Requirements

Fundamental knowledge of FPGA, Verilog HDL, and computer architecture

Proficiency in Zynq FPGA and Vivado development

Proficiency in Python programming and Linux command-line operations

Description

Do you want to learn AI acceleration on FPGA?This project-based online course offers practical insights into designing AI accelerators, specifically a CNN algorithm for handwritten digit classification. The focus of the course is on the system design level on how to integrate a CNN module (written in Verilog RTL) with the application processor running Linux. The final result of this project is a web application for taking a handwritten digit and then sending this data to be processed with the CNN accelerator on the FPGA. On average, a speedup factor of 12x is achieved by using this accelerator compared to the CPU.This course is not intended for absolute beginners in FPGA development. A basic understanding of FPGA design using Zynq and the PYNQ framework is expected before enrolling, as these fundamentals will not be covered. Familiarity with Verilog and Python is also required.What is CNN?A Convolutional Neural Network (CNN) is a type of deep learning model particularly well-suited for processing data with a grid-like structure, such as images. It works by automatically learning spatial hierarchies of features through layers that perform convolutions—mathematical operations that extract features like edges, textures, and shapes from the input data. These layers are typically followed by pooling layers, which reduce the spatial dimensions to make computation more efficient and prevent overfitting. The final layers are usually fully connected and perform classification or regression based on the learned features. CNNs are widely used in computer vision tasks like image recognition, object detection, and facial recognition due to their ability to capture spatial patterns effectively.What is CNN accelerator?CNNs require acceleration on FPGAs because they involve intensive computations, especially during convolution operations, which can be slow and power-hungry on general-purpose processors. FPGAs offer parallel processing, customizable architecture, and lower latency, making them ideal for speeding up CNN tasks while maintaining energy efficiency. This is particularly valuable for real-time applications like autonomous driving or edge devices where performance and power constraints are critical.Why use Zynq FPGA?The Zynq FPGA, developed by Xilinx, is well-suited for CNN acceleration due to its combination of programmable logic (FPGA fabric) and integrated ARM processors on a single chip. This hybrid architecture allows for high-performance parallel processing of CNN layers in the FPGA fabric while handling control and pre/post-processing tasks on the ARM cores. With its flexibility, low latency, and energy efficiency, Zynq enables efficient implementation of custom CNN accelerators, making it ideal for embedded and real-time applications like robotics, autonomous vehicles, and smart cameras.Start learning today—enroll now!All the source code is available within this course. After finishing the course, you will receive a certified certificate of completion. A complete Udemy 30-day money-back guarantee if you are not satisfied with this course, allowing you to study with no risk. See you within the course!

Overview

Section 1: Convolutional Neural Network

Lecture 1 Introduction to Convolutional Neural Network

Lecture 2 CNN Architecture for MNIST Digit

Lecture 3 CNN Model for MNIST Digit

Section 2: RTL Design of CNN Accelerator

Lecture 4 The RTL Module of Convolution Buffer

Lecture 5 The RTL Module of Convolution Calculation

Lecture 6 The RTL Module of Max Pooling and ReLU

Lecture 7 The RTL Module of Fully Connected

Lecture 8 CNN Layers Integration

Section 3: SoC Integration of CNN Accelerator

Lecture 9 The RTL Module of AXI Stream Wrapper

Lecture 10 Block Design of ZYNQ System

Lecture 11 Functional and Performance Testing

Section 4: Development of Embedded Web

Lecture 12 Backend and Frontend Development

University students developing AI acceleration projects on FPGA,FPGA engineers seeking hands-on experience with AI acceleration,Anyone interested in learning and building FPGA-based systems