  With the rapid development of artificial intelligence (AI) community,
education in AI is receiving more and more attentions. There have been many AI
related courses in the respects of algorithms and applications, while not many
courses in system level are seriously taken into considerations. In order to
bridge the gap between AI and computing systems, we are trying to explore how
to conduct AI education from the perspective of computing systems. In this
paper, a course practice in intelligent computing architectures are provided to
demonstrate the system education in AI era. The motivation for this course
practice is first introduced as well as the learning orientations. The main
goal of this course aims to teach students for designing AI accelerators on
FPGA platforms. The elaborated course contents include lecture notes and
related technical materials. Especially several practical labs and projects are
detailed illustrated. Finally, some teaching experiences and effects are
discussed as well as some potential improvements in the future.
