Building an MNIST NPU with FPGA — From RTL to Bit-True Verification

Many people have learned FPGA and Verilog but get stuck at the question, "So, how do I actually build an AI accelerator?" This course resolves that curiosity. Using just a single entry-level FPGA and free tools, you will start from a hand-designed 3×3 kernel, train a CNN yourself, quantize the trained weights to INT8 to load onto the hardware, and verify that the results are Bit-True—not deviating by even a single bit from the Python model. As a professor with 30 years of experience in the semiconductor industry, I teach only verified content as 'working circuits' on actual FPGAs. By gaining the experience of designing and proving it yourself rather than just learning theory, you will acquire practical skills that are recognized in the fabless design and AI semiconductor fields.

To design AI semiconductors such as NPUs, transformer accelerators, and mini LLMs yourself, you need to understand theoretically why the computations inside them occur. This course distills and condenses into three parts only the essential theory required for hardware design, covering everything from a single neuron to CNNs, transformers, and LLMs. Rather than memorizing formulas, a professor with 30 years of experience in the semiconductor industry connects software theory with hardware implementation through an intuition for “why it works that way.”

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

We will summarize the theoretical concepts of MNIST, the most fundamental dataset in computer vision, and learn how to implement it using Torchvision's API as well as how to implement a custom dataset class directly.

It goes without saying that AI artificial intelligence is the trend. But what can you use machine learning and deep learning for? Now, let's learn useful Internet of Things IoT Computer Vision projects that are used in daily life.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

This is a lecture that studies Vision Transformer, one of the latest deep learning technologies, and implements a paper using Pytorch. Come experience the new future of the vision field with me!

Starting from the operating principles of RISC-V instructions, this is a hands-on CPU design course in which you directly design a 32-bit CPU with a five-stage pipeline, completing the process from Verilog RTL implementation and simulation verification to running it on a physical FPGA. Among the 40 RV32I instructions, 14 instructions essential for learning the core structure of a CPU are selected to progressively build TinyRV32I. You will directly design and verify the key structures that make up a real CPU, including the datapath, pipeline registers, ALU and control circuits, branches, hazards, stalls and flushes, and memory-mapped I/O. The Verilog RTL, testbenches, assembly programs, memory images, and FPGA constraint files used in the course are provided. Ultimately, you will run actual programs on an Arty S7-25 FPGA using the CPU you designed. This course does not end here. After completing the core CPU structure through TinyRV32I, the series continues as a step-by-step CPU design program in which you expand it into a Full RV32I CPU supporting all 40 RV32I instructions in the follow-up course.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

This course is for those who have learned Verilog syntax but are stuck wondering, "So, what should I build?" With just a single FPGA board, you will directly design four types of hardware: a digital clock, an ultrasonic distance sensor, an OLED display, and real-time camera video. The difficulty progresses from output to input/control, display, and finally video, allowing you to master digital design through hands-on experience with visible results. An active professor with 30 years of experience in the semiconductor industry teaches only content that has been verified on actual FPGAs.

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

If Season 1 was about checking your skills and solidifying the basics, Season 2 is intensive practical training focused solely on 'passing and career transitions.' Beyond simple syntax or gate-level design, you will directly design top-tier, high-difficulty modules (CDC, Synchronous/Asynchronous FIFO, AMBA APB, SPI, I2C, FIR filters, etc.) that frequently appear in coding tests and actual workflows at major corporations and foreign companies. Do you need solid design capabilities that won't waver even under a recruiter's relentless follow-up questions? This course contains all the practical know-how and ultimate coding test problems from ETA, who successfully moved from a regional university to a global semiconductor firm. In this final season, complete your career transition free pass.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

When you decide to build a CPU, you usually start by looking for a circuit diagram. But the moment you unfold the schematic, questions arise that you cannot answer. Why does this block need to be here? Why are there two ports for reading registers? The answer lies entirely in the instructions. Once you know what the instructions require, the circuits you need are determined. It does not work the other way around. There are already many RISC-V courses. But most of them display values on slides, say "This is how it works," and move on. Students have no way to verify whether those values are correct. This course comes with a Python instruction-execution simulator. Type run ADD, and the machine code is assembled on the spot, the 32 bits are broken down by field, and how the registers change appears on the screen. Every number in the textbook is the result of running this tool, and it can be reproduced identically on your screen. Commercial debuggers require licenses, and simulators are difficult to install. This tool only requires Python. No board, license, or installation is needed. You will execute all 40 RV32I instructions yourself without leaving out a single one, discovering along the way what the circuit needs to do.

This lecture implements a DDR Memory Arbiter.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

UART is the most basic and widely utilized serial communication method, making it a very suitable learning topic for beginners new to FPGA design. In this course, we will thoroughly understand the communication principles and frame structure of UART, and based on this, we will proceed with directly implementing transmit/receive logic through RTL (Register Transfer Level) design. By implementing UART on an FPGA, you can systematically develop your digital circuit design skills and gain experience verifying its operation at the actual hardware level through simulation and verification. Furthermore, by learning the method utilizing the MicroBlaze soft processor and Xilinx UART IP, you will gain a broad understanding of processor-based system design flow and interface configuration. This will enable you to acquire comprehensive development capabilities encompassing both logic design and processor design.

This lecture implements a DDR Memory Arbiter.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.