TwinDNN: A Tale of Two Deep Neural Networks

Hyunmin Jeong; Deming Chen

doi:10.1109/FCCM51124.2021.00061

TwinDNN: A Tale of Two Deep Neural Networks

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Machine learning is one of the most popular fields in the current era. It is used in various areas, such as speech recognition, face recognition, medical diagnosis, etc. However, the problem is that the neural networks for machine learning applications are becoming too large and slow as they get more complicated and powerful. This problem gets especially serious when neural networks are used for edge devices with a small chip. As a result, researchers have proposed two major solutions to solve this problem.

Original language	English (US)
Title of host publication	Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	276
Number of pages	1
ISBN (Electronic)	9780738126739
DOIs	https://doi.org/10.1109/FCCM51124.2021.00061
State	Published - May 2021
Event	29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021 - Virtual, Orlando, United States Duration: May 9 2021 → May 12 2021

Publication series

Name	Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021

Conference

Conference	29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021
Country/Territory	United States
City	Virtual, Orlando
Period	5/9/21 → 5/12/21

Keywords

Hardware Accelerator
High Level Synthesis
Machine Learning
Neural Network Quantization

ASJC Scopus subject areas

Software
Artificial Intelligence
Computer Science Applications
Hardware and Architecture

Online availability

10.1109/FCCM51124.2021.00061

Library availability

Discover UIUC Full Text

Cite this

Jeong, H., & Chen, D. (2021). TwinDNN: A Tale of Two Deep Neural Networks. In Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021 (pp. 276). Article 9443658 (Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FCCM51124.2021.00061

TwinDNN: A Tale of Two Deep Neural Networks. / Jeong, Hyunmin; Chen, Deming.
Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 276 9443658 (Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jeong, H & Chen, D 2021, TwinDNN: A Tale of Two Deep Neural Networks. in Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021., 9443658, Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 276, 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021, Virtual, Orlando, United States, 5/9/21. https://doi.org/10.1109/FCCM51124.2021.00061

Jeong H, Chen D. TwinDNN: A Tale of Two Deep Neural Networks. In Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 276. 9443658. (Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021). doi: 10.1109/FCCM51124.2021.00061

@inproceedings{7012578ce03a4374a92db6681fa722cd,

title = "TwinDNN: A Tale of Two Deep Neural Networks",

abstract = "Machine learning is one of the most popular fields in the current era. It is used in various areas, such as speech recognition, face recognition, medical diagnosis, etc. However, the problem is that the neural networks for machine learning applications are becoming too large and slow as they get more complicated and powerful. This problem gets especially serious when neural networks are used for edge devices with a small chip. As a result, researchers have proposed two major solutions to solve this problem.",

keywords = "Hardware Accelerator, High Level Synthesis, Machine Learning, Neural Network Quantization",

author = "Hyunmin Jeong and Deming Chen",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021 ; Conference date: 09-05-2021 Through 12-05-2021",

year = "2021",

month = may,

doi = "10.1109/FCCM51124.2021.00061",

language = "English (US)",

series = "Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "276",

booktitle = "Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021",

address = "United States",

}

TY - GEN

T1 - TwinDNN

T2 - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021

AU - Jeong, Hyunmin

AU - Chen, Deming

PY - 2021/5

Y1 - 2021/5

N2 - Machine learning is one of the most popular fields in the current era. It is used in various areas, such as speech recognition, face recognition, medical diagnosis, etc. However, the problem is that the neural networks for machine learning applications are becoming too large and slow as they get more complicated and powerful. This problem gets especially serious when neural networks are used for edge devices with a small chip. As a result, researchers have proposed two major solutions to solve this problem.

AB - Machine learning is one of the most popular fields in the current era. It is used in various areas, such as speech recognition, face recognition, medical diagnosis, etc. However, the problem is that the neural networks for machine learning applications are becoming too large and slow as they get more complicated and powerful. This problem gets especially serious when neural networks are used for edge devices with a small chip. As a result, researchers have proposed two major solutions to solve this problem.

KW - Hardware Accelerator

KW - High Level Synthesis

KW - Machine Learning

KW - Neural Network Quantization

UR - http://www.scopus.com/inward/record.url?scp=85107625088&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85107625088&partnerID=8YFLogxK

U2 - 10.1109/FCCM51124.2021.00061

DO - 10.1109/FCCM51124.2021.00061

M3 - Conference contribution

AN - SCOPUS:85107625088

T3 - Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021

SP - 276

BT - Proceedings - 29th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2021

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 May 2021 through 12 May 2021

ER -

TwinDNN: A Tale of Two Deep Neural Networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Cite this