TY - GEN
T1 - Project-Based Learning
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
AU - Sun, Heming
AU - Yu, Lu
N1 - Funding Information:
Heming Sun is with Waseda University, Japan; Zhejiang University, China; JST PRESTO, Japan. Lu Yu is with Zhejiang University, and also with Zhe-jiang Provincial Key Laboratory of Information Processing, Communication and Networking, China. This work was supported in part by JST, PRESTO Grant Number JPMJPR19M5, Japan.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Neural network has shown its powerful ability in many research fields in the recent years. By using different network structures, many new algorithms are developed to enhance the accuracy. Along with the algorithm development, corresponding architectures are also proposed for the acceleration. However, pure algorithm may not be hardware friendly. As a result, we need to find an optimal trade-off between algorithmic accuracy and architectural efficiency. To help students build the gap between algorithm and architecture, this paper introduces a project-based learning. The project is called learned image compression, which is composed of three phases: algorithm design, architecture mapping and algorithm-architecture co-optimization. Through the project, the students are expected to develop a neural network with high image compression ratio and hardware performance. Furthermore, these kind of knowledge can be extended to any neural network applications.
AB - Neural network has shown its powerful ability in many research fields in the recent years. By using different network structures, many new algorithms are developed to enhance the accuracy. Along with the algorithm development, corresponding architectures are also proposed for the acceleration. However, pure algorithm may not be hardware friendly. As a result, we need to find an optimal trade-off between algorithmic accuracy and architectural efficiency. To help students build the gap between algorithm and architecture, this paper introduces a project-based learning. The project is called learned image compression, which is composed of three phases: algorithm design, architecture mapping and algorithm-architecture co-optimization. Through the project, the students are expected to develop a neural network with high image compression ratio and hardware performance. Furthermore, these kind of knowledge can be extended to any neural network applications.
KW - algorithm
KW - architecture
KW - Education
KW - learned image compression
KW - project-based learning
UR - http://www.scopus.com/inward/record.url?scp=85142516166&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142516166&partnerID=8YFLogxK
U2 - 10.1109/ISCAS48785.2022.9937978
DO - 10.1109/ISCAS48785.2022.9937978
M3 - Conference contribution
AN - SCOPUS:85142516166
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 2127
EP - 2131
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 May 2022 through 1 June 2022
ER -