Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design

Hyun Kim

doi:10.1109/ISOCC53507.2021.9613926

Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design

Hyun Kim

Dept. of Electrical and Information Engineering

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

4 Scopus citations

Abstract

Recently, many attempts have been made to use convolutional neural network (CNN) applications on mobile devices. This paper presents the overall process of designing optimal CNN accelerators for mobile devices based on algorithm, architecture, and memory system co-design, and introduces various existing studies related to each research field.

Original language	English
Title of host publication	Proceedings - International SoC Design Conference 2021, ISOCC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	237-238
Number of pages	2
ISBN (Electronic)	9781665401746
DOIs	https://doi.org/10.1109/ISOCC53507.2021.9613926
State	Published - 2021
Event	18th International System-on-Chip Design Conference, ISOCC 2021 - Jeju Island, Korea, Republic of Duration: 6 Oct 2021 → 9 Oct 2021

Publication series

Name	Proceedings - International SoC Design Conference 2021, ISOCC 2021

Conference

Conference	18th International System-on-Chip Design Conference, ISOCC 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	6/10/21 → 9/10/21

Keywords

Convolutional neural networks
Hardware Accelerator
Low-power memory
Mobile device
Network Compression

Access to Document

10.1109/ISOCC53507.2021.9613926

Cite this

Kim, H. (2021). Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design. In Proceedings - International SoC Design Conference 2021, ISOCC 2021 (pp. 237-238). (Proceedings - International SoC Design Conference 2021, ISOCC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC53507.2021.9613926

@inproceedings{c109390bc18c491b850ec092159fdd6b,

title = "Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design",

abstract = "Recently, many attempts have been made to use convolutional neural network (CNN) applications on mobile devices. This paper presents the overall process of designing optimal CNN accelerators for mobile devices based on algorithm, architecture, and memory system co-design, and introduces various existing studies related to each research field.",

keywords = "Convolutional neural networks, Hardware Accelerator, Low-power memory, Mobile device, Network Compression",

author = "Hyun Kim",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th International System-on-Chip Design Conference, ISOCC 2021 ; Conference date: 06-10-2021 Through 09-10-2021",

year = "2021",

doi = "10.1109/ISOCC53507.2021.9613926",

language = "English",

series = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

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

pages = "237--238",

booktitle = "Proceedings - International SoC Design Conference 2021, ISOCC 2021",

}

Kim, H 2021, Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design. in Proceedings - International SoC Design Conference 2021, ISOCC 2021. Proceedings - International SoC Design Conference 2021, ISOCC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 237-238, 18th International System-on-Chip Design Conference, ISOCC 2021, Jeju Island, Korea, Republic of, 6/10/21. https://doi.org/10.1109/ISOCC53507.2021.9613926

Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design. / Kim, Hyun.
Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 237-238 (Proceedings - International SoC Design Conference 2021, ISOCC 2021).

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

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T1 - Implementation of Optimal CNN Accelerators for Mobile Devices

T2 - 18th International System-on-Chip Design Conference, ISOCC 2021

AU - Kim, Hyun

PY - 2021

Y1 - 2021

N2 - Recently, many attempts have been made to use convolutional neural network (CNN) applications on mobile devices. This paper presents the overall process of designing optimal CNN accelerators for mobile devices based on algorithm, architecture, and memory system co-design, and introduces various existing studies related to each research field.

AB - Recently, many attempts have been made to use convolutional neural network (CNN) applications on mobile devices. This paper presents the overall process of designing optimal CNN accelerators for mobile devices based on algorithm, architecture, and memory system co-design, and introduces various existing studies related to each research field.

KW - Convolutional neural networks

KW - Hardware Accelerator

KW - Low-power memory

KW - Mobile device

KW - Network Compression

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U2 - 10.1109/ISOCC53507.2021.9613926

DO - 10.1109/ISOCC53507.2021.9613926

M3 - Conference contribution

AN - SCOPUS:85123384776

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SP - 237

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BT - Proceedings - International SoC Design Conference 2021, ISOCC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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Kim H. Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design. In Proceedings - International SoC Design Conference 2021, ISOCC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 237-238. (Proceedings - International SoC Design Conference 2021, ISOCC 2021). doi: 10.1109/ISOCC53507.2021.9613926

Implementation of Optimal CNN Accelerators for Mobile Devices: Algorithm, Architecture, and Memory System Co-Design

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