Towards Systems Education for Artificial Intelligence: A Course Practice in Intelligent Computing Architectures

TL;DR

This paper presents a course on intelligent computing architectures aimed at teaching students how to design AI accelerators on FPGA platforms, bridging AI and system-level education.

Contribution

It introduces a novel course practice focusing on system-level AI education, including practical labs and projects for FPGA-based AI accelerator design.

Findings

Students gained hands-on experience in FPGA-based AI accelerator design.

The course improved understanding of system-level AI concepts.

Teaching methods showed positive feedback from students.

Abstract

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.