eprintid: 19887
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/98/87
datestamp: 2024-06-04 14:19:37
lastmod: 2024-06-04 14:19:37
status_changed: 2024-06-04 14:16:07
type: article
metadata_visibility: show
creators_name: Lu, C.-K.
creators_name: Liew, W.S.
creators_name: Tang, T.B.
creators_name: Lin, C.-H.
title: Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection
ispublished: pub
keywords: Application programs; Computer aided diagnosis; Computer hardware; Convolution; Electric power utilization; Field programmable gate arrays (FPGA); Network layers; Neural networks, Cancer; Colorectal cancer; Convolutional neural network; Embedded device; Field programmable gate array; Field programmables; Hardware; Polyp detection; Power demands; Programmable gate array, Diseases
note: cited By 0
abstract: The increasing rates of colorectal cancer and associated mortality have attracted interest in the use of computer-aided diagnosis tools based on artificial intelligence (AI) for the detection of polyps at an early stage. Most AI models are implemented on software platforms; however, due to the demands of embedded devices, hardware implementations have to fulfill the demands of real-time applications with better accuracy and low-power consumption. In this letter, we propose an optimized four-layer network that can be implanted into an embedded device and determine the feasibility of implanting our convolutional neural network (CNN) into a microprocessor. The essential functions of the CNN (i.e., padding, convolution, ReLU, max-pooling, fully connected, and softmax layers) are implemented in the microprocessor. The proposed method achieves efficient classification with high performance and takes only 2.5488 mW at a working frequency of 8 MHz. We conclude this letter with a discussion of the results and future direction of research. © 2009-2012 IEEE.
date: 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7
id_number: 10.1109/LES.2023.3234973
full_text_status: none
publication: IEEE Embedded Systems Letters
volume: 16
number: 1
pagerange: 5-8
refereed: TRUE
issn: 19430663
citation:   Lu, C.-K. and Liew, W.S. and Tang, T.B. and Lin, C.-H.  (2024) Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection.  IEEE Embedded Systems Letters, 16 (1).  pp. 5-8.  ISSN 19430663