[2025 Fall] Embedded Systems and Labs
Course Information
| Course | Embedded Systems and Labs | Department | Computer Science and Engineering |
|---|---|---|---|
| Office Hours | TBA | Course No. and Class | 37271-02 |
| Hours | 3.0 | Academic Credit | 3.0 |
| Professor | Yoon, Myung Kuk | Office | Asan Engineering Building, 105-3 |
| Telephone | (82)-2-3277-3819 | myungkuk.yoon at ewha.ac.kr | |
| Value of Competence | Pursuit of Knowledge(70), Creative Convergence(30) | Keyword | Computer System, System Software, Real-Time System |
| Class Time | (Wed) 12:30 ~ 15:15 |
Course Description
-
This course explores the fundamentals and advanced techniques of embedded systems using the Jetson Nano platform. It is designed to provide students with a strong foundation in embedded systems while delving into advanced C programming and the CUDA language for leveraging the power of NVIDIA GPUs. * The primary syllabus used for this course is from the website rather than the traditional school system. Consequently, any significant updates or changes will be made exclusively to the web syllabus. * Some lecture materials may contain Korean; however, the primary language for this course is English. Most materials and exams will be predominantly provided in English. * Due to limited seats and available embedded boards, the number of students for this course is strictly capped. This count will not change, even if you send an email. PLEASE DO NOT REQUEST AN INCREASE IN THE NUMBER OF SEATS FOR THIS COURSE.
Prerequisites
- Basic knowledge of the C/C++ programming language is required.
- Strongly recommended only for students who have completed the Digital Logic Design and Computer Architecture course to take this class.
Course Format
| Lecture | Discussion/Presentation | Experiment/Practicum | Field Study | Other |
|---|---|---|---|---|
| 40% | 0% | 60% | 0% | 0% |
Course Objectives
In this class, students will be introduced to:
- Embedded Systems
- Linux
- CUDA Programming
- Neural Network
- And more topics if time permits
Evaluation System
Evaluation: Relative + Absolute
| Midterm Exam | Final Exam | Quizzes | Presentations | Projects | Assignment | Participation | Other |
|---|---|---|---|---|---|---|---|
| 35% | 35% | 0% | 0% | 0% | 30% | 0% | 0% |
- About 40% of students: A (Including A+/A/A-)
- About 40% of students: B (Including B+/B/B-)
- About 20% of students: C and below
- If your total score is above 30% but does not exceed 40%, you will receive a “D” regardless of the percentage above.
- If your total score does not exceed 30%, you will receive an “F” regardless of the percentage above.
- You may be absent up to three times. If you exceed three absences, you will receive an “F” for the course. No excuses for absences will be accepted under any circumstances. Please note that the first class (the first week) will not be counted toward your absence total.
- If you are late twice, you are considered absent once.
- Complete your assignments and exams independently. Any instances of plagiarism, whether from fellow students or online sources, will result in an automatic 'F' in this course, regardless of your current standing.
Required Materials
The lecture material will be made available on Cyberampus.
Supplementary Materials
NONE
Optional Additional Readings
NONE
Course Contents
| Week | Date | Topics & Materials | Assignement & Quiz & Etc. |
|---|---|---|---|
| Week #01 | 2025/09/03 (Wed) | Lecture #01: Introduction to the Course | |
| Week #02 | 2025/09/10 (Wed) | Lecture #02: Linux OS & Linux Commands | |
| Week #03 | 2025/09/17 (Wed) | Lecture #03: Computer Architecture & Development Environment | |
| Week #04 | 2025/09/24 (Wed) | Lecture #04: Graphics Processing Units & Vector Addition | |
| Week #05 | 2025/10/01 (Wed) | Lecture #05: Image Blur (CPU) | |
| Week #06 | 2025/10/08 (Wed) | NO CLASS (Korean Thanksgiving Day) | |
| Week #07 | 2025/10/15 (Wed) | Lecture #06: GPU Hardware Architecture + Image Blur (GPU) | |
| Week #08 | 2025/10/22 (Wed) | Lecture #07: Fully-Connected MNIST NN (CPU) | |
| 2025/10/25 (Sat) | Lecture #09: Midterm Exam | 11:00AM ~ 12:30PM (TBD) | |
| Week #09 | 2025/10/29 (Wed) | Lecture #08: Fully-Connected MNIST NN (GPU) | |
| Week #10 | 2025/11/05 (Wed) | Lecture #10: Profiling + Batch | |
| Week #11 | 2025/11/12 (Wed) | Lecture #11: 2D Convolution + General Matrix Multiply (GEMM) | |
| Week #12 | 2025/11/19 (Wed) | Lecture #12: CUDA Stream | |
| Week #13 | 2025/11/26 (Wed) | Lecture #13: Unified Virtual Memory (UVM) | |
| Week #14 | 2025/12/03 (Wed) | Lecture #14: Summary + Image Classification | |
| Week #15 | 2025/12/10 (Wed) | Lecture #15: TBD | |
| 2025/12/13 (Sat) | Lecture #16: FINAL EXAM | 11:00AM ~ 12:30PM (TBD) | |
| Week #16 | 2025/12/17 (Wed) | Final Exam Review (Nonmandatory) |
Course Policies
For laboratory courses, all students are required to complete lab safety training.
Special Accommodations
According to the University regulation #57, students with disabilities can request special accommodation related to attendance, lectures, assignments, and/or tests by contacting the course professor at the beginning of semester. Based on the nature of the students’ requests, students can receive support for such accommodations from the course professor and/or from the Support Center for Students with Disabilities (SCSD).
Extra Information
The contents of this syllabus are not final—they may be updated.