[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 E-Mail 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:

  1. Basic knowledge of the C/C++ programming language is required.
  2. 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:

  1. Embedded Systems
  2. Linux
  3. CUDA Programming
  4. Neural Network
  5. 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:

  1. About 35% of students: A (Including A+/A/A-)
  2. About 45% of students: B (Including B+/B/B-)
  3. About 20% of students: C and below

Further details regarding letter grades and attendance:

  1. If your total score is above 20% but does not exceed 30%, you will receive a ā€œDā€ regardless of the percentage above.
  2. If your total score does not exceed 20%, you will receive an ā€œFā€ regardless of the percentage above.
  3. You may be absent up to three times. If you exceed three absences, you will receive an ā€œFā€ 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.
  4. If you are late twice, you are considered absent once.
  5. 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)