Performance analysis of all-optical logical gate using artificial neural network

Hamedi, Samaneh; Jahromi, Hamed Dehdashti

doi:10.1016/j.eswa.2021.115029

Cited by 12 publications

(4 citation statements)

References 40 publications

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“…This methodology is useful for evaluating various architectures for embedded chips and associated applications like hardware accelerators. The ANN was modeled for various logic functions and logic gates [ 26 ]. One of the gates utilized for serval applications and quick modeling is the XOR gate.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of multiple input single layer neural network hardware chip

Goel

Kumar

2023

Multimed Tools Appl

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Section: Related Workmentioning

confidence: 99%

Performance analysis of multiple input single layer neural network hardware chip

Goel

Kumar

2023

Multimed Tools Appl

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“…This work opened up the possibility of exploring various machine learning-based algorithms to model AO-logic devices with high accuracy and bring down the computational time utilized by existing commercial softwares from several hours/minutes to the range of milliseconds. 77 Two optical neural networks composed of passive optical elements were utilized to achieve AO-XOR operation with low power consumption. 78 A summary of the various techniques to design AO-LGs is shown in Table 1.…”

Section: Artificial Neural Network-based Designmentioning

confidence: 99%

Review on all-optical logic gates: design techniques and classifications – heading toward high-speed optical integrated circuits

Anagha

Jeyachitra

2022

Opt. Eng.

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All-optical logic gates (AO-LGs) are the key elements that play a pivotal role in the development of future all-optical networks and all-optical computing. A complete overview of the seven all-optical logic gates (i.e., AND, OR, NOT, XOR, XNOR, NAND, and NOR) based on their design techniques and applications are covered, including the latest technologies, such as topological photonics and artificial intelligence-based designs techniques. In addition, we have further categorized the AO-LGs as reconfigurable gates, simultaneous gates, reversible gates, modulation-based gates, and data rate-based gates. The techniques to implement these different classes of gates are reviewed and their limitations are discussed. We also discuss in brief the various simulation tools used to design and analyze the AO-LGs. Finally, the most feasible techniques for constructing optical integrated circuits based on the existing fabrication technologies and available resources are explored, and future prospects are outlined.

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“…ANN has become a favorite, massively parallel information processing system (Moradi et al, 2021;Dadgar, 2021;Kaviani and Sohn, 2021). ANNs imitate the information processing structure or functionalities of biological neural networks by utilizing many coupled and simple artificial neurons (Gately, 1996;Hamedi and Jahromi, 2021). Numerous recent studies demonstrate that ANN is a robust technique that is applicable to a wide variety of applications, including supply chain benchmarking (Kuo et al, 2010), project management (Li and Liu, 2012), scheduling (Tirkel, 2013), quality improvement (Carlucci et al, 2013), demand forecasting (Panapakidis and Dagoumas, 2017), and renewable energy technologies (Rezaee et al, 2018;Sharifi et al, 2019).…”

Section: The Efficiency Of Canadian Universitiesmentioning

confidence: 99%

“…, 2021; Dadgar, 2021; Kaviani and Sohn, 2021). ANNs imitate the information processing structure or functionalities of biological neural networks by utilizing many coupled and simple artificial neurons (Gately, 1996; Hamedi and Jahromi, 2021). Numerous recent studies demonstrate that ANN is a robust technique that is applicable to a wide variety of applications, including supply chain benchmarking (Kuo et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Jauhar

Zolfagharinia²,

Amin³

2022

BIJ

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PurposeThis research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.Design/methodology/approachThe authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.FindingsThe major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.Research limitations/implicationsThis paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.Practical implicationsThe findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.Originality/valueThis research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

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Performance analysis of all-optical logical gate using artificial neural network

Cited by 12 publications

References 40 publications

Performance analysis of multiple input single layer neural network hardware chip

Performance analysis of multiple input single layer neural network hardware chip

Review on all-optical logic gates: design techniques and classifications – heading toward high-speed optical integrated circuits

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

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