[2026 Fall] Embedded Systems and Labs
Course Information
| Course | Embedded Systems and Labs | ||
|---|---|---|---|
| Department | Computer Science and Engineering | ||
| Office Hours | TBA | Course/Class No. | 37271-02 |
| Hours | 3.0 | Academic Credit | 3.0 |
| Professor | Yoon, Myung Kuk | ||
| Office | Asan Engineering Building, 337 | ||
| 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 | ||
| Teaching Assistant | Siyeon Kang | TA E-Mail | kstar1029 at ewhain.net |
| Teaching Assistant | SeJin Park | TA E-Mail | stronger at ewhain.net |
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.
Other Important Information
- 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.
- Students must bring their own personal laptop (Windows, Mac, or Linux) with the required software installed to participate in the labs. Please note that there is no contingency plan for students who are unable to complete labs because they do not have a laptop.
Prerequisites
Students should meet the following requirements:
- 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 |
|---|---|---|---|---|
| 50% | 0% | 50% | 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 |
|---|---|---|---|---|---|---|---|
| 0% | 40% | 60% | 0% | 0% | 0% | 0% | 0% |
Grade distribution:
- About 35% of students: A (Including A+/A/A-)
- About 45% of students: B (Including B+/B/B-)
- About 20% of students: C and below
Further details regarding letter grades and attendance:
- If your total score is above 20% but does not exceed 30%, you will receive a āDā regardless of the percentage above.
- If your total score does not exceed 20%, you will receive an āFā regardless of the percentage above.
- You may be absent up to three times. If you exceed three absences, you will receive an āFā 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 | Etc. |
|---|---|---|---|
| #01 | Sep. 2 (Wed.) |
Lecture #01: Introduction to the Course | |
| #02 | Sep. 9 (Wed.) |
Lecture #02: Linux OS & Linux Commands | |
| #03 | Sep. 16 (Wed.) |
Lecture #03: Computer Architecture & Development Environment | |
| #04 | Sep. 23 (Wed.) |
Lecture #04: Neural Network & Image Classification | |
| #05 | Sep. 30 (Wed.) |
Lecture #05: Graphics Processing Units & Vector Addition | |
| #06 | Oct. 7 (Wed.) |
Lecture #06: Image Blur (CPU) | 1st Quiz |
| #07 | Oct. 14 (Wed.) |
Lecture #07: Image Blur (GPU) | |
| #08 | Oct. 21 (Wed.) |
Lecture #08: Convolution (CPU) | 2nd Quiz |
| #09 | Oct. 28 (Wed.) |
Lecture #09: Convolution (GPU) | |
| #10 | Nov. 4 (Wed.) |
NO CLASS | |
| #11 | Nov. 11 (Wed.) |
Lecture #10: Fully Connected MNIST (CPU) | 3rd Quiz |
| #12 | Nov. 18 (Wed.) |
Lecture #11: Fully Connected MNIST (GPU) | |
| #13 | Nov. 25 (Wed.) |
Lecture #12: General Matrix Multiply (GEMM) | 4th Quiz |
| #14 | Dec. 2 (Wed.) |
Lecture #13: Convolutional Neural Network (CNN) | |
| #15 | Dec. 9 (Wed.) |
Lecture #14: Summary | |
| Dec. 12 (Sat.) |
Lecture #15: Final Exam | 11:00AM ~ 12:30PM (TBD) |
|
| #16 | Dec. 16 (Wed.) |
Final Exam Review (Nonmandatory) |
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