TY  - JOUR
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301246&doi=10.1109%2fLES.2023.3234973&partnerID=40&md5=21ca087f3e2ccd1abc4e6f6c19d424b7
PB  - Institute of Electrical and Electronics Engineers Inc.
SP  - 5
IS  - 1
N1  - cited By 0
A1  - Lu, C.-K.
A1  - Liew, W.S.
A1  - Tang, T.B.
A1  - Lin, C.-H.
Y1  - 2024///
SN  - 19430663
TI  - Implementation of a Convolutional Neural Network Into an Embedded Device for Polyps Detection
ID  - scholars19887
AV  - none
VL  - 16
JF  - IEEE Embedded Systems Letters
N2  - 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.
KW  - Application programs; Computer aided diagnosis; Computer hardware; Convolution; Electric power utilization; Field programmable gate arrays (FPGA); Network layers; Neural networks
KW  -  Cancer; Colorectal cancer; Convolutional neural network; Embedded device; Field programmable gate array; Field programmables; Hardware; Polyp detection; Power demands; Programmable gate array
KW  -  Diseases
EP  - 8
ER  -