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

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.

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.

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.

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 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.

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."

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.

The semiconductor industry is a representative manufacturing sector where production facilities, control systems, partners, and data are closely interconnected. For this reason, even a small security incident or system failure can lead to production shutdowns, quality issues, delivery delays, and technology leaks. This lecture explains the OT security threats that can occur at semiconductor manufacturing sites in a way that even beginners can easily understand. Rather than focusing on complex technical explanations, it centers on what security problems can arise in the actual field, why the semiconductor industry is a target for attacks, and how security incidents impact companies. Furthermore, it examines how security threats are evolving as manufacturing environments become more intelligent with the spread of AI and smart factories.

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.

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.

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.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

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

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

✅ 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.

This course examines major security threats and incident cases that can occur in the process control environment of the chemical industry, providing an easy-to-understand approach to AI-based anomaly detection and response. Participants will learn practical OT security, focusing on risks unique to the chemical industry such as production shutdowns, manipulation of process values, and hazardous substance leaks.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

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.

Complete Understanding of Robotics High- and Low-Level Controllers, Sensors, and Ultra-High-Speed Communication Concepts Through the Unitree Go2 Example

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

If you don’t have time to watch the entire 32-hour lecture, quickly grasp the concepts you need for the exam first. This lecture explains complex concepts in simple terms so you can understand a vast amount of content more quickly, and organizes the key flow intuitively so it can be understood at a glance. Let’s efficiently work toward passing the written exam by mastering the essentials first.

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.

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.