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Performance Analysis of Deep Neural Networks for Object Classification with Edge TPU

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dc.citedby6
dc.contributor.authorAsyraaf Jainuddin A.A.en_US
dc.contributor.authorHou Y.C.en_US
dc.contributor.authorBaharuddin M.Z.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid57220803880en_US
dc.contributor.authorid37067465000en_US
dc.contributor.authorid35329255600en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2023-05-29T08:08:02Z
dc.date.available2023-05-29T08:08:02Z
dc.date.issued2020
dc.descriptionBenchmarking; Computer aided instruction; Deep neural networks; Image coding; Large dataset; Learning systems; Neural networks; Data transferring; Data-transmission speed; Hardware selection; Machine learning applications; Neural network model; Object classification; Performance analysis; Processing units; Deep learningen_US
dc.description.abstractDeep learning becomes a more popular, widespread, and common tool in almost any task that requires information extraction from a large dataset. Hence, the data transmission speed between the data-gathering devices and processing units can be crucial in hardware selection depending on the machine learning application. Generally, the processing unit is usually centralized, and the data transferring time will increase when the data-gathering devices were installed further away from the processing unit. The work aims to provide the performance analysis on Google's new machine learning hardware called Edge TPU that was created specifically for edge devices. Furthermore, the work also reviewed the different types of deep neural network models as current benchmarks in deep learning were tested with different hardware used in edge applications. The review also discussed the comparison of the performance of the edge device using the deep neural networks in Tensorflow. From the results obtained, the performance of the edge device with the Edge TPU is faster than the device without it. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243367
dc.identifier.doi10.1109/ICIMU49871.2020.9243367
dc.identifier.epage328
dc.identifier.scopus2-s2.0-85097651125
dc.identifier.spage323
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097651125&doi=10.1109%2fICIMU49871.2020.9243367&partnerID=40&md5=6eeda9b1976329af80f39ffb4d74fb3a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25309
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titlePerformance Analysis of Deep Neural Networks for Object Classification with Edge TPUen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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