SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

Abdelrahman Saeed; Ayman Tawfik; Hassan Mostafa; Ahmed Hussein Khalil

doi:10.1109/MECO58584.2023.10154967

SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

Abdelrahman Saeed
, Ayman Tawfik
, Hassan Mostafa
, Ahmed Hussein Khalil

Cairo University
Zewail City of Science and Technology

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

3 Scopus citations

Abstract

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).

Original language	English
Title of host publication	12th Mediterranean Conference on Embedded Computing, MECO 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350322910
DOIs	https://doi.org/10.1109/MECO58584.2023.10154967
State	Published - 2023
Event	12th Mediterranean Conference on Embedded Computing, MECO 2023 - Budva, Montenegro Duration: 6 Jun 2023 → 10 Jun 2023

Publication series

Name	12th Mediterranean Conference on Embedded Computing, MECO 2023

Conference

Conference	12th Mediterranean Conference on Embedded Computing, MECO 2023
Country/Territory	Montenegro
City	Budva
Period	6/06/23 → 10/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Breast Cancer Detection
Convolutional Neural Networks (CNN)
Deep-learning Processing Unit (DPU)
Field Programmable Gate Array (FPGA)

Access to Document

10.1109/MECO58584.2023.10154967

Cite this

Saeed, A., Tawfik, A., Mostafa, H., & Khalil, A. H. (2023). SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm. In 12th Mediterranean Conference on Embedded Computing, MECO 2023 (12th Mediterranean Conference on Embedded Computing, MECO 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MECO58584.2023.10154967

@inproceedings{d3df259159114473b0da616a4cdd3e06,

title = "SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm",

abstract = "Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5\% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).",

keywords = "Breast Cancer Detection, Convolutional Neural Networks (CNN), Deep-learning Processing Unit (DPU), Field Programmable Gate Array (FPGA)",

author = "Abdelrahman Saeed and Ayman Tawfik and Hassan Mostafa and Khalil, \{Ahmed Hussein\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th Mediterranean Conference on Embedded Computing, MECO 2023 ; Conference date: 06-06-2023 Through 10-06-2023",

year = "2023",

doi = "10.1109/MECO58584.2023.10154967",

language = "English",

series = "12th Mediterranean Conference on Embedded Computing, MECO 2023",

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

booktitle = "12th Mediterranean Conference on Embedded Computing, MECO 2023",

address = "United States",

}

Saeed, A, Tawfik, A, Mostafa, H & Khalil, AH 2023, SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm. in 12th Mediterranean Conference on Embedded Computing, MECO 2023. 12th Mediterranean Conference on Embedded Computing, MECO 2023, Institute of Electrical and Electronics Engineers Inc., 12th Mediterranean Conference on Embedded Computing, MECO 2023, Budva, Montenegro, 6/06/23. https://doi.org/10.1109/MECO58584.2023.10154967

SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm. / Saeed, Abdelrahman; Tawfik, Ayman; Mostafa, Hassan et al.
12th Mediterranean Conference on Embedded Computing, MECO 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (12th Mediterranean Conference on Embedded Computing, MECO 2023).

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

TY - GEN

T1 - SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

AU - Saeed, Abdelrahman

AU - Tawfik, Ayman

AU - Mostafa, Hassan

AU - Khalil, Ahmed Hussein

PY - 2023

Y1 - 2023

N2 - Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).

AB - Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field Programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU).

KW - Breast Cancer Detection

KW - Convolutional Neural Networks (CNN)

KW - Deep-learning Processing Unit (DPU)

KW - Field Programmable Gate Array (FPGA)

UR - https://www.scopus.com/pages/publications/85164971860

U2 - 10.1109/MECO58584.2023.10154967

DO - 10.1109/MECO58584.2023.10154967

M3 - Conference contribution

AN - SCOPUS:85164971860

T3 - 12th Mediterranean Conference on Embedded Computing, MECO 2023

BT - 12th Mediterranean Conference on Embedded Computing, MECO 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th Mediterranean Conference on Embedded Computing, MECO 2023

Y2 - 6 June 2023 through 10 June 2023

ER -

SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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