For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| 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. | |||||||||
| 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. | |||||||||
| ECE5988 | GPU Architecture Cornerstone | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | - | 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. | |||||||||
| ECE5989 | Network System SW Design | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| As the performance of network hardware has improved significantly, it is important to understand the performance of system software for network processing. In this course, we learn the core designs of the current network software stacks including Linux kernel stacks and the most recent user-space stacks for high performance. We also have a term-project that designs and implements our own network protocols and algorithms (e.g., TCP congestion control) in Linux kernel. The recommended prerequisites for this course include computer networks and C programming language. | |||||||||
| ECE5989 | Network System SW Design | 3 | 6 | Major | Master/Doctor | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| As the performance of network hardware has improved significantly, it is important to understand the performance of system software for network processing. In this course, we learn the core designs of the current network software stacks including Linux kernel stacks and the most recent user-space stacks for high performance. We also have a term-project that designs and implements our own network protocols and algorithms (e.g., TCP congestion control) in Linux kernel. The recommended prerequisites for this course include computer networks and C programming language. | |||||||||
| ECE5990 | Special Topics In Electrical and Computer Engineering | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course surveys a wide variety of recent advanced topics in electrical and computer engineering. The students can grasp the flow of the latest topics in electrical and computer engineering field, discover new research topics, and use them as the basis for their research. | |||||||||