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dc.contributor.authorKrestinskaya, O.
dc.contributor.authorSalama, Khaled N.
dc.contributor.authorJames, A. P.
dc.date.accessioned2020-10-07T13:48:46Z
dc.date.available2020-10-07T13:48:46Z
dc.date.issued2020-09-29
dc.identifier.citationKrestinskaya, O., Salama, K., & James, A. P. (2020). Towards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search. 2020 IEEE International Symposium on Circuits and Systems (ISCAS). doi:10.1109/iscas45731.2020.9180514
dc.identifier.isbn978-1-7281-3321-8
dc.identifier.issn0271-4302
dc.identifier.doi10.1109/ISCAS45731.2020.9180514
dc.identifier.urihttp://hdl.handle.net/10754/665484
dc.description.abstractThe selection of hyperparameters and circuit components for optimum hardware implementation of a neural network is a challenging task, which has not been automated yet. This work proposes the method for the selection of optimum neural network architecture and hyperparameters using genetic algorithm based on the hardware-related performance metrics, such an on-chip area, power consumption, processing time and robustness to hardware non-idealities, and focus on memristor-based analog network architecture. The experimental results show that the proposed approach allows to select the optimum architecture based on the designers' preferences.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9180514/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9180514/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9180514
dc.rightsArchived with thanks to IEEE
dc.subjectMemristor
dc.subjectNeural Networks
dc.subjectAnalog circuit
dc.subjectHyperparameter selection
dc.subjectGenetic algorithm
dc.titleTowards Hardware Optimal Neural Network Selection with Multi-Objective Genetic Search
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentSensors Lab
dc.conference.date12-14 Oct. 2020
dc.conference.name2020 IEEE International Symposium on Circuits and Systems (ISCAS)
dc.conference.locationSevilla
dc.eprint.versionPost-print
dc.contributor.institutionNazarbayev University, Nur-Sultan, Kazakhstan
dc.contributor.institutionMaker Village; IIITM; Kerala Govt.; Kerala, India
kaust.personSalama, Khaled N.
refterms.dateFOA2020-10-08T06:51:06Z
dc.date.published-online2020-09-29
dc.date.published-print2020-10


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