The Modeling and Hardware Implementation of Semiconductor Circuit Elements by Using ANN and FPGA

Tuntaş, Remzi

doi:10.12693/aphyspola.128.b-78

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Tuntas proposed ANN for modelling and implementing semiconductor circuit elements with very high-speed integrated circuit hardware description language (VHDL) [7]. It increases the efficiency of the circuit model.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet

Leavline,

Krishnasamy

2024

Preprint

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A human behaviour imitation in the computing industry can be obtained using Neural Network architectures. One of the popular and influential neural network architecture is multilayer back propagation network (MLBPN). This paper aims to design and implement the MLBPN model for analyzing medical images. Many earlier research works have focused on implementing machine and deep learning models for analyzing medical data. But the computational speed is less and requires more epochs to obtain accurate results. Some researchers have overcome the challenges by using different integrated circuit techniques which are not cost-effective. This paper implements the multi layer perceptron (MLP) architecture using a double gate metal oxide semiconductor field effect transistor (DGMOSFET) model. The physical and logical functionalities of the MOSFET are integrated with MLP to provide the similar electrical behaviour of the transistor to control the current and voltage. The proposed MLP with DG-MOSFET model is trained with datasets having mixed of disease cases which were taken from FDA-NCI Clinical Proteomics Program Databank and the testing process is evaluated. The simulation is carried out with MATLAB and Cadence software, and then the analysed values are compared with actual values. The output proves that the speed and accuracy are improved compared with single gate devices.

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Section: Literature Reviewmentioning

confidence: 99%

Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet

Leavline,

Krishnasamy

2024

Preprint

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“…Günümüzde oldukça popüler sayısal sinyal işleme platformlarından birisi olan FPGA (Alan(da)/Saha(da) Programlanabilir Kapı Dizileri-Field Programmable Gate Array) çiplerinin diğer sayısal platformlara göre düşük güç tüketimi, yüksek çalışma frekansları, tekrar tekrar programlanabilme, hızlı ilk prototipleme, paralel çalışma ve esneklik gibi önemli avantajları bulunmaktadır. Bu nedenle sinyal-görüntü işleme (Paukštaitis ve Dosinas, 2009), modelleme (Tuntas, 2015), kaos (Koyuncu ve ark., 2018), rasgele sayı üretimi (Alçın ve ark., 2019), algoritma hızlandırma (Sahin, 2010)…”

Section: Fpga çIpleriunclassified

FPGA Tabanlı LogSig ve TanSig Transfer Fonksiyonlarının IQ-Math Sayı Standardında Tasarımı ve Gerçeklenmesi

AKÇAY>

Koyuncu

Alçın

et al. 2022

Journal of Materials and Mechatronics: A

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Artificial Neural Networks (ANNs) have been used extensively in engineering fields where many processes such as optimization, prediction, signal processing, decision making and control today. Transfer Functions (TF) used in these operations affect directly the output of the result by affecting the ANN structure. In the first stage of this study, apart from the studies in the literature, non-linear LogSig and TanSig TF have been coded using VHDL in accordance with the 32-bit (16I-16Q) IQ-Math standard. The exponential function e^n, which is common to these two TF structures, has been designed using the CORDIC-LUT approach. In the second stage, an FPGA-based sample three-input-three-output ANN has been performed. 8 neurons have been used in the hidden layer of this design. LogSig and TanSig TF have been used in the hidden layer and PureLin TF has been used in the output layer. The sample ANN has been coded using VHDL with 32-bit IQ-Math standard for two different TF. A separate testbench file has been created for each design, and all these designs have been tested using VHDL with the Xilinx ISE DS. For the simulation results obtained, MSE and RMSE error analyzes were performed using numerical-based LogSig-TanSig TF and ANN designs, and the results were presented. Then, each design has been synthesized for the XC7K70T-3FBG676 FPGA (Kintex-7), and the chip statistics have been presented by performing the Place-Route process. As a result, ANN-LS (Artificial Neural Networks-LogSig) design produced more successful results with 8.86E-06 MSE and 2.98E-03 RMSE error analysis results. In future studies, real-time ANN applications can be realized on FPGA chips by using these ANN and TF designs.

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“…ANNs have the ability to establish relationships between the inputs and outputs and produce results. ANN can be trained with current examples to model the problem and the results can be used in real time [11][12][13][14].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

Boran

Diren

2017

Acta Phys. Pol. A

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Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. In this study, the proposed model is expected to detect the source(s) for out-of-control signals without help of an expert in the process, by using a multilayer neural network. This model was implemented in furniture fasteners manufacturing. Time gain was obtained while detecting source(s) for out-of-control signals.

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The Modeling and Hardware Implementation of Semiconductor Circuit Elements by Using ANN and FPGA

Cited by 9 publications

References 14 publications

Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet

Integration of Artificial Neural Network Model for Smart Medical System Using Double Gate Mosfet

FPGA Tabanlı LogSig ve TanSig Transfer Fonksiyonlarının IQ-Math Sayı Standardında Tasarımı ve Gerçeklenmesi

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

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