Joint Validation of Application Models and Multi-Abstraction Network-on-Chip Platforms

Author(s):  
Sanna Määttä ◽  
Leandro Möller ◽  
Leandro Soares Indrusiak ◽  
Luciano Ost ◽  
Manfred Glesner ◽  
...  

Application models are often disregarded during the design of multiprocessor Systems-on-Chip (MPSoC). This is due to the difficulties of capturing the application constraints and applying them to the design space exploration of the platform. In this article we propose an application modelling formalism that supports joint validation of application and platform models. To support designers on the trade-off analysis between accuracy, observability, and validation speed, we show that this approach can handle the successive refinement of platform models at multiple abstraction levels. A case study of the joint validation of a single application successively mapped onto three different platform models demonstrates the applicability of the presented approach.

Author(s):  
Sanna Määttä ◽  
Leandro Möller ◽  
Leandro Soares Indrusiak ◽  
Luciano Ost ◽  
Manfred Glesner ◽  
...  

Application models are often disregarded during the design of multiprocessor Systems-on-Chip (MPSoC). This is due to the difficulties of capturing the application constraints and applying them to the design space exploration of the platform. In this article we propose an application modelling formalism that supports joint validation of application and platform models. To support designers on the trade-off analysis between accuracy, observability, and validation speed, we show that this approach can handle the successive refinement of platform models at multiple abstraction levels. A case study of the joint validation of a single application successively mapped onto three different platform models demonstrates the applicability of the presented approach.


Author(s):  
Laura Ziegler ◽  
Kemper Lewis

A unique set of cognitive and computational challenges arise in large-scale decision making, in relation to trade-off processing and design space exploration. While several multi-attribute decision making methods exist in the current design literature, many are insufficient or not fully explored for many-attribute decision problems of six or more attributes. To address this scaling in complexity, the methodology presented in this paper strategically elicits preferences over iterative attribute subsets while leveraging principles of the Hypothetical Equivalents and Inequivalents Method (HEIM). A case study demonstrates the effectiveness of the approach in the construction of a systematic representation of preferences and the convergence to a single ‘best’ alternative.


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