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On applying multiple criteria decision analysis in embedded systems design.

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Title: On applying multiple criteria decision analysis in embedded systems design.
Authors: Sapienza, Gaetana1 gaetana.sapienza@se.abb.com, Brestovac, Goran2, Grgurina, Robi2, Seceleanu, Tiberiu1
Source: Design Automation for Embedded Systems. Sep2016, Vol. 20 Issue 3, p211-238. 28p.
Subjects: Multiple criteria decision making, Embedded computer system design & construction, Systems design, Parallel algorithms, Surveys
Abstract: We focus here on the application of multi critera decision analysis (MCDA) techniques in hardware/software partitioning activities to be used in the design and deployment of embedded systems. Our goal is to identify the best existing methods and tools suitable to support the approach we have taken for the partitioning process. We provide this via a survey of the most well-known MCDA methods and tools (for a specific class of MCDA methods called multi attribute decision making. We identify a set of criteria that need to be addressed, in some way, by the methods, and implemented by related tools. These '11-suitability criteria' help us in deciding the appropriateness of the analysed methods and tools for the envisaged partitioning approach. In brief, we are interested that the MCDA methods are taking into account multiple extra-functional properties, expressed by a variety of types, with possible missing values, should enable dependency handling, decision traceability, etc. The conclusion is that there are criteria that are not fulfilled by any of the methods, and hence there is no method or tool that can directly used for the partitioning. However, the results shows the potential of using MCDA in the partitioning process and provide a good starting point for future research activities. [ABSTRACT FROM AUTHOR]
Copyright of Design Automation for Embedded Systems is the property of Springer Nature 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.)
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: On applying multiple criteria decision analysis in embedded systems design.
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  Data: <searchLink fieldCode="JN" term="%22Design+Automation+for+Embedded+Systems%22">Design Automation for Embedded Systems</searchLink>. Sep2016, Vol. 20 Issue 3, p211-238. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Embedded+computer+system+design+%26+construction%22">Embedded computer system design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Surveys%22">Surveys</searchLink>
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  Data: We focus here on the application of multi critera decision analysis (MCDA) techniques in hardware/software partitioning activities to be used in the design and deployment of embedded systems. Our goal is to identify the best existing methods and tools suitable to support the approach we have taken for the partitioning process. We provide this via a survey of the most well-known MCDA methods and tools (for a specific class of MCDA methods called multi attribute decision making. We identify a set of criteria that need to be addressed, in some way, by the methods, and implemented by related tools. These '11-suitability criteria' help us in deciding the appropriateness of the analysed methods and tools for the envisaged partitioning approach. In brief, we are interested that the MCDA methods are taking into account multiple extra-functional properties, expressed by a variety of types, with possible missing values, should enable dependency handling, decision traceability, etc. The conclusion is that there are criteria that are not fulfilled by any of the methods, and hence there is no method or tool that can directly used for the partitioning. However, the results shows the potential of using MCDA in the partitioning process and provide a good starting point for future research activities. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Design Automation for Embedded Systems is the property of Springer Nature 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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        Value: 10.1007/s10617-016-9171-7
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        Text: English
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      – SubjectFull: Embedded computer system design & construction
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      – SubjectFull: Systems design
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      – SubjectFull: Parallel algorithms
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              M: 09
              Text: Sep2016
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