ECE 554
Coure Inforamtion
| Instructor | Dr. Weiwen Jiang |
|---|---|
| wjiang8@gmu.edu | |
| Lecture Time | Thursday 4:30 pm – 7:10 pm |
| In-person Session Location | Exploratory Hall L102 |
| Distance-learning Session Location | Zoom |
| Office Hour | Monday, Wednesday 14:00 - 15:00 |
| Place | Room 3247, Nguyen Engineering Building |
| TA (1) | Jinyang Li |
|---|---|
| jli56@gmu.edu | |
| Office Hour (1) | Tuesday 11:30 - 13:30 |
| Place | Room 3208, Nguyen Engineering Building |
| Office Hour (2) | Friday 10:00 - 12:00 |
| Place | Room 3204, Nguyen Engineering Building |
| TA (2) | Chiranjivan Krishnakumar Nirmala |
|---|---|
| ckrishn@gmu.edu |
Course Materials
Course materials will be posted before or after the class. No formal textbook is required. This e-book will be referred to on the course.
Prerequisites
CS 222 and ECE 231 and ECE 350 with the minimum grade of C
The course topics are self-contained so that a background in machine learning is not required. Students should be familiar with programming and embedded systems to complete the course projects.
Course Description
Machine learning (ML) has gradually become the core component of wide applications in different computing scenarios, ranging from edge computing to cloud computing. This course focuses on resource-constrained edge computing, in particular the embedded systems, and introduces techniques for developing energy/time efficient ML algorithms and models for the embedded systems. Topics that are covered include (i) commonly used ML algorithms, (ii) ML model compression techniques, (iii) hardware-aware machine learning, (iv) hardware and neural architecture co-design. The course also provides a comprehensive team-based research and development experience through projects and presentations. Offered by Electrical & Comp. Engineering. May not be repeated for credit.
Tools for Lab
- Google Colab
- Xilinx High-Level Synthesis
Schedule and Documents
| W | Date | Topic | Documents | Note |
|---|---|---|---|---|
| 1 | Jan 18 | Course Information & Introduction to Machine Learning | [Lab1] | |
| 2 | Jan 25 | Train Neural Networks | ||
| 3 | Feb 01 | Deep Convolutional Neural Networks (CNN) | ||
| 4 | Feb 08 | Deep Convolutional Neural Networks (CNN) - Part 2 | ||
| 5 | Feb 15 | Natural Langue Processing | ||
| 6 | Feb 22 | Reinforcement Learning | ||
| 7 | Mar 14 | ML Accelerator Design | ||
| 8 | Mar 21 | ML System Implementation and Optimization | ||
| 9 | Mar 28 | Mid-Term Exam | ||
| 10 | Apr 04 | Model Compression | ||
| 11 | Apr 11 | DNN Design and Compression Review | ||
| 12 | Apr 18 | Neural Architecture Search | ||
| 13 | Apr 25 | HW/SW Co-Design with Neural Architecture Search | ||
| 14 | May 02 | Course Project Demonstration |
