TY - GEN
T1 - Towards systems education for artificial intelligence
T2 - 30th Great Lakes Symposium on VLSI, GLSVLSI 2020
AU - Yang, Jianlei
AU - Gao, Xiaopeng
AU - Zhao, Weisheng
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Intelligent Computing Architectures
KW - Neural Network Accelerators
KW - System Education
UR - https://www.scopus.com/pages/publications/85091269070
U2 - 10.1145/3386263.3406935
DO - 10.1145/3386263.3406935
M3 - 会议稿件
AN - SCOPUS:85091269070
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 567
EP - 572
BT - GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PB - Association for Computing Machinery
Y2 - 7 September 2020 through 9 September 2020
ER -