Introduction to AI Semiconductors: Mastering Everything with Excel, No EDA Tools Required

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.

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.

When people decide to build a CPU, they usually start by looking for a circuit diagram. However, the moment they unfold the schematic, unanswerable questions arise: Why must this block be here? Why are there two ports for reading registers? The answers all lie within the instructions. Once you know what an instruction requires, the necessary circuitry is determined. It doesn't work the other way around. There are already many RISC-V lectures available. However, most of them simply display values on a slide, say "This is how it works," and move on. Students have no way to verify if those values are correct. This course provides a Python-based instruction execution simulator. When you type `run ADD`, the machine code is assembled on the spot, the 32 bits are laid out by field, and you can see exactly how the registers change on your screen. Every number in the textbook is a result of this tool's execution, and it can be reproduced identically on your screen. Commercial debuggers require licenses, and simulators often have installation barriers. This tool only requires Python. You don't need a board, a license, or a complex setup. This is a process of executing all 40 RV32I instructions without skipping a single one, discovering for yourself what the circuit needs to do.

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."

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.

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.

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.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

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.

When people decide to build a CPU, they usually start by looking for a circuit diagram. However, the moment they unfold the schematic, unanswerable questions arise: Why must this block be here? Why are there two ports for reading registers? The answers all lie within the instructions. Once you know what an instruction requires, the necessary circuitry is determined. It doesn't work the other way around. There are already many RISC-V lectures available. However, most of them simply display values on a slide, say "This is how it works," and move on. Students have no way to verify if those values are correct. This course provides a Python-based instruction execution simulator. When you type `run ADD`, the machine code is assembled on the spot, the 32 bits are laid out by field, and you can see exactly how the registers change on your screen. Every number in the textbook is a result of this tool's execution, and it can be reproduced identically on your screen. Commercial debuggers require licenses, and simulators often have installation barriers. This tool only requires Python. You don't need a board, a license, or a complex setup. This is a process of executing all 40 RV32I instructions without skipping a single one, discovering for yourself what the circuit needs to do.

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.

You will systematically learn the principles of how the CPU, memory, and disk collaborate, as well as how the operating system mediates resources. You will acquire practical foundational knowledge to read server specification sheets and identify performance bottlenecks, establishing a basis for learning about cloud and network technologies.

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.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.

No Coding Skills Required: Build Your Very First Service with Google Stitch An All-in-One AI Development Workflow That Seamlessly Connects Every Step from Planning to Deployment

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

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

This course is designed for those who use AI but are confused about how much they can trust it. It covers how to use AI in your work or creative process without overdoing it, ensuring it provides actual value. Rather than requiring professional development knowledge or complex tools, it focuses on how anyone can effectively integrate AI into their own workflow. Instead of teaching complex prompt engineering, the course focuses on the mindset of **knowing when to use AI and when it's better not to**, as well as **how to verify and refine AI outputs** rather than trusting them blindly. Through this course, you will establish criteria for using AI not as an automation tool that replaces everything, but as a **practical partner** that helps organize your thoughts and assists in your work.

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

This special lecture concurrently prepares for Microsoft AI-900 certification, featuring the latest content reflecting May 2025 exam scope, combining theory and practice, including AI overview, machine learning basics, computer vision, natural language processing, document intelligence, knowledge mining, and generative AI.

Do you feel stuck every time you have to write a report? Wasting time not knowing what to write, revising over and over because the logic isn't clear, and barely finishing right before the deadline—this is a routine many professionals and students repeat. You may have tried using ChatGPT, but it often stops at simply writing sentences for you. It might produce "plausible writing," but it doesn't create a "result-driven report." This lecture is different. We will show you how to use ChatGPT not just as a task-delegation tool, but as a strategic partner that amplifies your planning capabilities. ✔ How to clearly structure problem definitions ✔ How to apply frameworks to design logical flow ✔ How to strengthen persuasiveness based on data ✔ Strategies to upgrade to performance-oriented reports During the lecture, you will actually complete one of your own work reports, providing a practical experience that leads to immediate results rather than simple theory. Furthermore, by utilizing the latest features like Canvas and file upload analysis, we help you build AI literacy to stay ahead in the rapidly evolving generative AI environment. After taking this course: Your report writing time will decrease, Your logic will become more solid, And your results will become clearer. Experience the moment when report writing is no longer a burden, but a weapon that proves your capabilities.

Mastering Robotics High-Level/Low-Level Controllers, Sensors, and High-Speed Communication Concepts through Unitree Go2 Examples

In the midst of daily overwhelming tasks and endless repetitive work, you've probably wondered, "Isn't there a smarter and more efficient way to do this?" Haven't you felt frustrated wasting time on drafting reports, repeatedly failing to find new insights in brainstorming meetings, and struggling to keep up with competitors' pace? Right now, the most powerful tool to solve those concerns has emerged. It's Generative AI. This isn't just a trending technology. It's the most fundamental productivity revolution of our time, and those who first capture and utilize this change will become the winners of future business. This course will teach you how to immediately apply Generative AI, which will become your most capable assistant on your desk, to your actual work. Let's set aside complex theories for now. We focus on **"So what can I do with this technology starting tomorrow?"** 💡 We'll help you reclaim your time and creativity. Our core goal is your **'work automation'** and **'creativity maximization'**. 90% reduction in report writing time: Learn how to delegate the creation of reports, proposals, and email drafts that consume your precious time every week to AI. Simply input key data and direction, and AI will immediately complete professional documents for you. You can now focus on being a strategic thinker and leader, not a document writer. Ideas that outpace competitors: Need new marketing campaigns, innovative product designs, or content ideas that captivate customers? Practice using AI as a partner to instantly generate dozens of creative alternatives and predict market reactions. Data-driven smart decision making: Don't get lost in countless materials. AI quickly summarizes vast amounts of data and provides key insights, helping you make strategic judgments based on data rather than intuition. 🛠️ 'Prompts' are the new language. Utilizing Generative AI well ultimately means being good at **'prompt engineering'**. Like commanding a capable employee, the skill of giving clear and specific instructions to AI determines success or failure. In this course, we'll teach you the core formula for **"how to ask questions to get the desired results."** You'll directly write customized prompts optimized for your role - whether you're a marketer, developer, or planner - and experience how dramatically different the results can be. This knowledge will become a core future competency that won't waver no matter what new AI tools emerge. 🏆 Through this course, you'll become a 'future businessman'. Generative AI is no longer a 'choice' but a 'necessity'. If you reject this technology, your competitors will work 10 times faster every day. But if you seize this opportunity, you can be reborn as a 'future-ready talent' who maximizes productivity, leads creative innovation, and dramatically boosts business performance. Don't fear Generative AI. What matters is knowing how to utilize it well. Start your work innovation journey through this course. It will be the one decision that explodes your business potential. Register right now and experience a new way of working where you work less and achieve more.

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!

A course that helps you understand core concepts of computer architecture and trains you to explain them verbally in interviews.

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.