Courses

Graduate

  • ECE 595 “Online Course: Computer Vision for Embedded Systems”. Fall 2021 and 2022. This is an experimental course. This course provides an overview of running computer vision (OpenCV and PyTorch) on an embedded system. The course emphasizes the resource constraints imposed by embedded systems and examines methods (such as quantization and pruning) to reduce resource requirements. This course was offered in Spring 2022 as a Guest Lecturer (Sabbatical Visit) in “Special Topics in Machine Intelligence, Seoul National University”

    The course is available to anyone worldwide for auditing (through EdX, please check the link above).

Lecture

Topic

Slides

Videos

01

Introduction, OpenCV

Lecture 01

Video 01A, Video 01B, Video 01C

01 B

Quantization

Lecture 01B

02

Edge Detection, Segmentation

Lecture 02

Video 02A, Video 02B, Video 02C

03

Applications

Lecture 03

Video 03A, Video 03B, Video 03C, Video 03D

04

Machine Learning

Lecture 04

Video 04A, Video 04B, Video 04C

05

Modular Neural Networks

Lecture 05

Video 05A, Video 05B, Video 05C

06

Review and Write Papers

Lecture 06

Video 06A, Video 06B, Video 06C, Video 06D, Video 06E

07

Performance and Resources

Lecture 07

Video 07A, Video 07B, Video 07C, Video 07D

07 B

Pytorch Quantization

Lecture 07B

08

Detect and Track Objects

Lecture 08

Video 08A, Video 08B, Video 08C

09

Data Bias and Privacy

Lecture 09

Video 09A, Video 09B, Video 09C , Video 09D

10

Data Generation

Lecture 10

Video 10A, Video 10B

11

Neural Architecture Search

Lecture 11

Video 11A, Video 11B

12

Transformer A

Lecture 12

Video 12A, Video 12B, Video 12C, Video 12D

13

Transformer B

Lecture 13

Video 13A, Video 13B

14

Real-Time Scheduling

Lecture 14

Video 14A, Video 14B, Video 14C, Video 14D, Video 14E

15

Research Topics

Lecture 15

Video 15A, Video 15B

Undergraduate

Vertically Integrated Projects

Vertically Integrated Projects. VIP teams mix students from cohorts (first-year undergraduate to doctoral) and conduct research. Dr. Lu advises the following teams:

  • Fall 2023 “Artificial Intelligence in Music”: AI-enabled tools to support string music performers. The first tool, the Evaluator, aims to improve individual practice and performance. It analyzes a musician’s sound and compares it to digitized music scores to detect deviations in intonation, rhythm, and dynamics and suggest better posture based on sample performers’ recording with correct posture. The second tool, the Companion, plays the part of one or several instruments to replace absent musicians with matching tempo, and style of the human musicians through audio analysis of their performance while also responding in real-time to verbal instructions.

  • Fall 2023 “Computer Vision for Embedded Systems”: Investigate methods to improve efficiency (inference time, training time, storage space, energy consumption) of computer vision (both image and multimedia) so that computer vision can run on embedded systems. The team will evaluate how existing methods (such as quantization and pruning) can be applied to new neural architectures (such as transformers). The team will also investigate new architectures of neural networks and compare their efficiency with different levels of accuracy.

  • Spring 2023 and Fall 2022 “VIP Team: Computer Vision for Embedded Systems”: improve efficiency (inference time, training time, storage space, energy consumption) of computer vision (both image and multimedia) so that computer vision can run on embedded systems. The team will evaluate how existing methods (such as quantization and pruning) can be applied to new neural architectures (such as transformers). The team will also investigate new architectures of neural networks and compare their efficiency with different levels of accuracy. Advisor: Yung-Hsiang Lu.

  • Fall 2021 “Analyze Drone Video”: creates a dataset captured by drone (also called UAV, unmanned aerial vehicle) and a referee system that can evaluate the accuracy and performance (execution time) of different solutions. Sponsor: Facebook - Pytorch. Advisors: Qiang Qiu, Yung-Hsiang Lu, and Wei Zakharov.

  • Fall 2021 “Open-Source TensorFlow Software”: Creates software to be used in the TensorFlow 2 Model Garden as examples. Sponsor: Google. Advisors: James Davis and Yung-Hsiang Lu

  • Spring 2021 “Image Processing for Solar Sail”: Creates the software to analyze the images taken by the camera on a spacecraft using solar sail. Sponsor: NASA. Advisors: Alina Alexeenko, Anthony Cofer, Yung Hsiang Lu.

  • Spring 2021 “Program Analysis as a Service”: Creates an online service that analyzes computer programs to help students learn programming. Advisors: Aravind Machiry and Yung-Hsiang Lu.

ECE 264 Advanced C Programming

  • Fall 2023 “ECE 264 Advanced C Programming”

  • Spring 2023 “ECE 264 Advanced C Programming”. Topics covered: stack memory, recursion, memory management, structures, file (text and binary), dynamic structures (linked list and binary tree). Tools: gcc, gcov, Makefile, gdb, valgrind. Lecture videos are available here.

  • Fall 2021 “ECE 264 Advanced C Programming”. Tools: gcc, gcov, Makefile, gdb, valgrind.

  • Fall 2020: Video, slides, and script

ECE 270 Introduction to Digital System Design

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