This course includes in-depth coverage on existing and emerging IoT application domains, machine learning and deep neural networks, GPU and FPGA programming and optimization techniques for deep learning acceleration, and various computing systems that facilitate the rapid realization and growth of IoT. Detailed topics include definition and characteristics of IoT; IoT enabling technologies; smart domains and applications; IoT systems; IoT design methodology; machine learning and deep learning; embedded GPU and FPGA for IoT; IoT servers and cloud; data analytics for IoT; cognitive computing; cognitive systems design; cognitive application workload; IoT security; hands-on learning experience to build IoT systems through the IBM Node-RED framework; and various case studies such as smart city, smart agriculture, and smart home. Machine problems working with Raspberry Pi, embedded system (FPGA and GPU), and Node-RED together with homework assignments will be given to reinforce the understanding and learning of the techniques and topics. Raspberry Pi 4 boards are available for students to check out and use for the entire semester. Embedded GPUs and FPGAs are available to support the course as well.
Lecture Time: Tuesdays and Thursdays 11 am - 12:20 pm
Lecture Location: ECEB 1015
Location: ECEB 4022 [There will be three lab sessions this semester]
- Session 1: Mondays 10 am - 11:55 am. TA: Mang Yu
- Session 2: Mondays 1 pm - 2:55 pm. TA: Yuhong Li, Junhao Pan
- Session 3: Wednesdays 1 pm - 2:55 pm. TA: Enliang (Jack) Li
- Lab 1 document: TBA
- HW 1 document: TBA
|Jan. 21||Lecture 01: Course overview, IoT definition & characteristics, impact of IoT, cognitive computing and applications||[slides]|
We use Piazza for Q & A.