For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ECE5980 | Advanced Topics in Semiconductor Devices and Circuits | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| In this course, advanced topics in the field of semiconductor devices and circuits are to be introduced and discussed. Various issues in novel devices and circuits/systems (e.g., Artificial intelligence semiconductor chips, bio-inspired/-integrated devices(DNA Memory) are the main topics in this course. | |||||||||
| ECE5981 | Neuromorphic Integrated Circuits and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| The purpose of this subject is to learn a variety of techniques for designing neuromorphic systems mimicking the structure of a biological brain and accelerating the computation of AI algorithms. After understanding the structure of a biological brain and the operation principle of AI algorithms, students will learn about the basic properties of memristive devices and CMOS circuits as important tools to implement neuromorphic systems. The lecture also deals with the characteristics of state-of-the-art neuromorphic integrated systems and learns how to design low-power high-performance neuromorphic systems. | |||||||||
| ECE5981 | Neuromorphic Integrated Circuits and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| The purpose of this subject is to learn a variety of techniques for designing neuromorphic systems mimicking the structure of a biological brain and accelerating the computation of AI algorithms. After understanding the structure of a biological brain and the operation principle of AI algorithms, students will learn about the basic properties of memristive devices and CMOS circuits as important tools to implement neuromorphic systems. The lecture also deals with the characteristics of state-of-the-art neuromorphic integrated systems and learns how to design low-power high-performance neuromorphic systems. | |||||||||
| ECE5982 | IoT System IC Design | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| This course is the fusion course to cover the seven key technologies of IoT System Integrated Circuits (IC), Sensor Device and Signal Processing, Wireleline/Wireless Connectivity, AI based Data Processging, Energy Harvesting and Power Management, Memory Design, Security, System Application as team teaching form. | |||||||||
| ECE5982 | IoT System IC Design | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course is the fusion course to cover the seven key technologies of IoT System Integrated Circuits (IC), Sensor Device and Signal Processing, Wireleline/Wireless Connectivity, AI based Data Processging, Energy Harvesting and Power Management, Memory Design, Security, System Application as team teaching form. | |||||||||
| ECE5983 | Circuits and Systems for 6G Communication | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course is the fusion course to cover key aspects of 6th generation mobile communication, including high-speed modulation and demodulation techniques, mobile and base-station transceiver architectures and design of various building-block circuits. | |||||||||
| ECE5983 | Circuits and Systems for 6G Communication | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This course is the fusion course to cover key aspects of 6th generation mobile communication, including high-speed modulation and demodulation techniques, mobile and base-station transceiver architectures and design of various building-block circuits. | |||||||||
| ECE5984 | Foundations of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| Machine Learning is the study of how to build computer systems that learn from experience. This course will give an overview of many models and algorithms used in modern machine learning, including generalized linear models, multi-layer neural networks, support vector machines, Bayesian belief networks, clustering, and reinforcement learning. | |||||||||
| ECE5984 | Foundations of Machine Learning | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| Machine Learning is the study of how to build computer systems that learn from experience. This course will give an overview of many models and algorithms used in modern machine learning, including generalized linear models, multi-layer neural networks, support vector machines, Bayesian belief networks, clustering, and reinforcement learning. | |||||||||
| ECE5985 | Quantum-Meta Optics | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course will improve a physical understanding of photons, which represent quantum concept of light. In particular, non-classical properties of photons such as spontaneous emission, quantum cryptography, and quantum teleportation are discussed. In addition, meta-surfaces and materials in integrated quantum optics are studied. | |||||||||
| ECE5986 | Digital Communication ICs and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course teaches fundamental electrical issues in the design of high-performance digital communication systems. The detailed topics include transmission line analysis; noise in digital systems, its effect on signaling, and methods for noise reduction; timing conventions; timing noise, its effect on systems, and methods for mitigating timing noise; synchronization issues and sychronizer design; clock and power distribution problems and techniques; building blocks of high-speed signaling systems (PLL, CDR, and I/O circuits). Prerequisites: electronic circuits, electromagnetics | |||||||||
| ECE5986 | Digital Communication ICs and Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | No |
| This course teaches fundamental electrical issues in the design of high-performance digital communication systems. The detailed topics include transmission line analysis; noise in digital systems, its effect on signaling, and methods for noise reduction; timing conventions; timing noise, its effect on systems, and methods for mitigating timing noise; synchronization issues and sychronizer design; clock and power distribution problems and techniques; building blocks of high-speed signaling systems (PLL, CDR, and I/O circuits). Prerequisites: electronic circuits, electromagnetics | |||||||||
| ECE5987 | Advanced Memory Systems | 3 | 6 | Major | Master/Doctor | 1-4 | English | Yes | |
| The memory system is critical to the performance of modern computers. As processors become more capable of handling large amounts of data, designing efficient memory systems becomes increasingly important. This course introduces the fundamental concepts of a memory hierarchy, which includes on-chip cache, main memory, and storage. We also go over the most recent research on each component of the memory systems. The goal of the course is to teach students how to design novel memory system architectures and evaluate design trade-offs using system-level simulation. | |||||||||
| ECE5987 | Advanced Memory Systems | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | English | Yes |
| The memory system is critical to the performance of modern computers. As processors become more capable of handling large amounts of data, designing efficient memory systems becomes increasingly important. This course introduces the fundamental concepts of a memory hierarchy, which includes on-chip cache, main memory, and storage. We also go over the most recent research on each component of the memory systems. The goal of the course is to teach students how to design novel memory system architectures and evaluate design trade-offs using system-level simulation. | |||||||||
| ECE5988 | GPU Architecture Cornerstone | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| Graphics Processing Units (GPUs) are the parallel processor that efficiently process large amounts of data. The GPUs are one of the key hardware that contribute the fast imrprovement of AI techniques n this class, we study the basic architecture of GPUs. Also, we have in-class presentations and discussions of the papers about the GPU architectures that were published in top-tier computer architecture conferences. By doing them, we study the recent research trends of GPU architectures. Also, we study the behavior of the GPUs with a GPU simulator. | |||||||||