Project-Based Learning: Bridging the Gap Between Algorithm and Architecture in Neural Network Course

Heming Sun, Lu Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2127-2131
Number of pages5
ISBN (Electronic)9781665484855
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, United States
Duration: 2022 May 272022 Jun 1

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2022-May
ISSN (Print)0271-4310

Conference

Conference2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Country/TerritoryUnited States
CityAustin
Period22/5/2722/6/1

Keywords

  • algorithm
  • architecture
  • Education
  • learned image compression
  • project-based learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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