RISC-V CPU Design for FPGA Implementation ① Learning RV32I Instruction Structure and Execution Principles with Python

To directly design AI semiconductors such as NPUs, Transformer accelerators, and mini LLMs, you must understand the theoretical reasons behind the operations occurring within them. This course distills the essential theories required for hardware design—from a single neuron to CNNs, Transformers, and LLMs—into a compact three-part series. Rather than memorizing formulas, you will gain an intuition for "why things work the way they do," as a current professor with 30 years of experience in the semiconductor industry bridges the gap between software theory and hardware implementation.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

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.

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 study machine learning, you learn how to improve accuracy. But the moment you try to deploy that model onto an actual chip, you are left with questions no one ever taught you to ask: How many multiplications does this model require? What does it need to remember, and how much? Can the target chip specifications handle these demands? Machine learning textbooks focus on accuracy, while semiconductor textbooks focus on circuits, yet resources bridging the two are scarce. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs—facing situations where the model with the highest accuracy simply wouldn't fit on the board. In this course, you will build six algorithms from scratch in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy for each. You will then translate these metrics into actual FPGA board resources. While knowing the number of multiplications alone doesn't give you a sense of scale, expressing it as a percentage of the board's resources makes it easy to assess. After completing nine labs and compiling the six models into a single table, an unexpected picture emerges. We spent nine sessions counting multiplications, only to find that multiplications weren't the actual bottleneck. From there, we move on to quantizing floating-point numbers to integers, managing bit widths, and even establishing criteria for grading circuits. An FPGA board is not required. The course can be completed using Python alone, with the board serving merely as a reference for translating the measured metrics.

This guide provides easy and detailed explanations of the core concepts and practical tips for RISC-V, the latest open-source-based CPU architecture.

The author of "The Structure and Principles of Arm Architecture for System Software Development" will help you master the core of the latest Arm architectures (Armv8-A, Armv7-A), the absolute fundamentals of system software!

FPGA implementation using Verilog

It explains the core concepts and practical tips of RISC-V, the latest open-source-based CPU architecture, in an easy and detailed manner.

This guide provides easy and detailed explanations of the core concepts and practical tips for RISC-V, the latest open-source-based CPU architecture.

Digital/Analog circuit design basics and practical applications shared by a current Samsung Electronics employee! Get everything from a circuit design glossary and tips on choosing a specific sub-role to strategies for job interviews!

The 0s and 1s we talk about, HIGH and LOW, logic gates and flip-flops— they all emerge from the physical event of analog signals crossing a threshold. If so, wouldn't understanding the world before logic— the movements created by current and voltage— be the true beginning of circuit design? This course takes that first step together.

The author of "Learning the Structure and Principles of the Linux Kernel Through Debugging" provides a thorough explanation of core process concepts in the latest Linux kernel (v6.1), which is most widely used in system software.

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.

Beyond the technical field of semiconductors, this book provides a multidimensional view of how a single company moves the world. Going beyond simple technical explanations, it analyzes why the United States, China, and Taiwan are clashing over semiconductors, and examines through real cases how TSMC created an overwhelming gap that dominates the market. Centered on founder Morris Chang's leadership and TSMC's success strategies, it offers an opportunity to understand technology and management in an integrated way.

✅ Among the series of (1) to (7), (1) Changes in the role of computers, hierarchical structure, abstraction, PC structure ✅ CS101 series lectures - From basic computer theory to the latest practical examples - Provides a wide range of basic knowledge

AI Tech DNA Series: Core Principles of Computer Basics from an AI Perspective is an introductory course designed to help you easily understand the fundamental computer concepts necessary for the AI era. By explaining basic principles such as CPU, memory, storage, networks, and data structures through real-life analogies and an AI-focused lens, it builds the solid foundational strength needed to advance into Python, AI, security, and the cloud.

You can learn about the basic structure and operating principles of computer hardware, design methods, and OS, and acquire the knowledge necessary to create good programs.

This lecture implements DDR Memory Arbiter.

This lecture is expected to help students build a foundation in computer architecture.

Starting from a single switch, by directly building devices that process calculations, memory, and commands one by one, you will gain a deep understanding of how the computers we use every day are constructed. Recommended for those who desire an integrated experience connecting core concepts of computer science. This lecture is the Korean version of the world-renowned Nand2Tetris course, structured with the original author's permission and request. Keywords: Nand, HDL, ALU, PC, RAM, CPU

This lecture explains the principles of computer architecture, which is the knowledge underlying programming, for non-majors. The goal of the lecture is to become familiar with the principles of program operation.

We explain the core concepts of computer architecture and operating principles that SW developers need to know, using easy-to-understand illustrations.

Bestseller 『Study Computer Architecture + Operating Systems on Your Own』 author's free lecture. A collection of computer architecture and operating systems books that developers must know.

Have you ever had a hard time because you didn't understand IT infrastructure (server, network, storage, etc.) technology? Do you want to understand how IT infrastructure is structured and what technologies are applied? This is the only lecture in Korea that explains the various technologies that make up IT infrastructure!