DICHA - DNN based Intelligent classification for Workload Characterization on Heterogeneous Architecture.
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| Title: | DICHA - DNN based Intelligent classification for Workload Characterization on Heterogeneous Architecture. |
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| Authors: | Sivaramakrishnan, R.1 sivaram6685@gmail.com, Senthilkumar, G.2 Gskkanchi@gmail.com |
| Source: | Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 6, p5297-5313. 17p. |
| Subject Terms: | *Machine learning, Artificial neural networks, Deep learning, Industry 4.0, Big data |
| Abstract: | Nowadays Heterogeneous system on-chip (HSoC) become highly essential. IoT, Industry 4.0, intelligent vehicles, embedded devices, and cyber-physical framework applications are broadly utilizing such equipment models for workload processing. These continuous applications include a miscellaneous set of workloads with various attributes which highly influences the computational cycles. Moreover, asset organization become a basic issue in HSoC. In this paper DNN based classifier is proposed for HSoC stages to predict ideal computational asset for every responsibility at runtime. Deep Neural Networks (DNN), with deep layers and extremely high element of boundaries, have exhibited get through learning capacity in Machine learning region. Nowadays DNN with Big Data input are driving another heading in enormous scope object acknowledgment. The proposed classifier analysed the execution of a few HSoC stages to comprehend the functioning guideline of ongoing responsibilities at runtime. The noticed attributes are outlined as continuous data set and the equivalent is used to train and test the DNN classifier. The proposed classifier is assessed on raspberry-pi HSoC and re-enacted on the python with ML library. Precision, throughput, affectability, selectivity measurements are distinguished to break down the exhibition of the proposed calculations. The proposed DICHA framework accomplished the precision up to 96% contrasted and outrageous ML predictor for and furthermore saved the execution energy up to 30% for real-time embedded benchmark workloads like MiBench, IoMT. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 160450985 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DICHA - DNN based Intelligent classification for Workload Characterization on Heterogeneous Architecture. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sivaramakrishnan%2C+R%2E%22">Sivaramakrishnan, R.</searchLink><relatesTo>1</relatesTo><i> sivaram6685@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Senthilkumar%2C+G%2E%22">Senthilkumar, G.</searchLink><relatesTo>2</relatesTo><i> Gskkanchi@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Qualitative+Inquiry%22">Turkish Online Journal of Qualitative Inquiry</searchLink>. 2021, Vol. 12 Issue 6, p5297-5313. 17p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays Heterogeneous system on-chip (HSoC) become highly essential. IoT, Industry 4.0, intelligent vehicles, embedded devices, and cyber-physical framework applications are broadly utilizing such equipment models for workload processing. These continuous applications include a miscellaneous set of workloads with various attributes which highly influences the computational cycles. Moreover, asset organization become a basic issue in HSoC. In this paper DNN based classifier is proposed for HSoC stages to predict ideal computational asset for every responsibility at runtime. Deep Neural Networks (DNN), with deep layers and extremely high element of boundaries, have exhibited get through learning capacity in Machine learning region. Nowadays DNN with Big Data input are driving another heading in enormous scope object acknowledgment. The proposed classifier analysed the execution of a few HSoC stages to comprehend the functioning guideline of ongoing responsibilities at runtime. The noticed attributes are outlined as continuous data set and the equivalent is used to train and test the DNN classifier. The proposed classifier is assessed on raspberry-pi HSoC and re-enacted on the python with ML library. Precision, throughput, affectability, selectivity measurements are distinguished to break down the exhibition of the proposed calculations. The proposed DICHA framework accomplished the precision up to 96% contrasted and outrageous ML predictor for and furthermore saved the execution energy up to 30% for real-time embedded benchmark workloads like MiBench, IoMT. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 5297 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Industry 4.0 Type: general – SubjectFull: Big data Type: general Titles: – TitleFull: DICHA - DNN based Intelligent classification for Workload Characterization on Heterogeneous Architecture. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sivaramakrishnan, R. – PersonEntity: Name: NameFull: Senthilkumar, G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13096591 Numbering: – Type: volume Value: 12 – Type: issue Value: 6 Titles: – TitleFull: Turkish Online Journal of Qualitative Inquiry Type: main |
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