Scenario-Based Design

@inproceedings{Kampenhout2017,

title = {Programming and analysing scenario-aware dataflow on a multi-processor platform},

author = {Reinier Kampenhout and Sander Stuijk and Kees Goossens},

year  = {2017},

date = {2017-03-01},

urldate = {2017-03-01},

booktitle = {Proc. Design, Automation and Test in Europe Conference and Exhibition (DATE)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kampenhout2015,

title = {A Scenario-Aware Dataflow Programming Model},

author = {R. Kampenhout and S. Stuijk and K. Goossens},

doi = {10.1109/DSD.2015.28},

year  = {2015},

date = {2015-08-01},

urldate = {2015-08-01},

booktitle = {Digital System Design (DSD), 2015 Euromicro Conference on},

pages = {25-32},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DSBGC13,

title = {Throughput-constrained DVFS for scenario-aware dataflow graphs},

author = {M. Damavandpeyma and S. Stuijk and T. Basten and M. Geilen and H. Corporaal},

doi = {10.1109/RTAS.2013.6531090},

issn = {1080-1812},

year  = {2013},

date = {2013-04-01},

urldate = {2013-04-01},

booktitle = {2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)},

pages = {175-184},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SGEPC13,

title = {Automated extraction of scenario sequences from disciplined dataflow networks},

author = {Firew Siyoum and Marc Geilen and Johan Eker and Carl Platen and Henk Corporaal},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

booktitle = {2013 Eleventh ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE 2013)},

pages = {47-56},

publisher = {IEEE Computer Society},

address = {USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{YGBSC12,

title = {Playing games with scenario- and resource-aware SDF graphs through policy iteration},

author = {Yang Yang and Marc Geilen and Twan Basten and Sander Stuijk and Henk Corporaal},

doi = {10.1109/DATE.2012.6176462},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

booktitle = {2012 Design, Automation & Test in Europe Conference & Exhibition (DATE)},

pages = {194-199},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{TGV11,

title = {Performance Model Checking Scenario-Aware Dataflow},

author = {Bart Theelen and Marc Geilen and Jeroen Voeten},

editor = {Uli Fahrenberg and Stavros Tripakis},

url = {https://doi.org/10.1007/978-3-642-24310-3_5},

doi = {10.1007/978-3-642-24310-3_5},

isbn = {978-3-642-24310-3},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Formal Modeling and Analysis of Timed Systems},

pages = {43–59},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

abstract = {Dataflow formalisms are useful for specifying signal processing and streaming applications. To adequately capture the dynamic aspects of modern applications, the formalism of Scenario-Aware Dataflow (SADF) was recently introduced, which allows analysis of worst/best-case and average-case performance across different modes of operation (scenarios). The semantic model of SADF integrates non-deterministic and discrete probabilistic behaviour with generic discrete time distributions. This combination is different from the semantic models underlying contemporary quantitative model checking approaches, which often assume exponentially distributed or continuous time or they lack support for expressing discrete probabilistic behaviour. This paper discusses a model-checking approach for computing quantitative properties of SADF models such as throughput, time-weighted average buffer occupancy and maximum response time. A compositional state-space reduction technique is introduced as well as an efficient implementation of this method that combines model construction with on-the-fly state-space reductions. Strong reductions are possible because of special semantic properties of SADF, which are common to dataflow models. We illustrate this efficiency with several case studies from the multi-media domain.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SGTB11,

title = {Scenario-aware dataflow: Modeling, analysis and implementation of dynamic applications},

author = {Sander Stuijk and Marc Geilen and Bart Theelen and Twan Basten},

doi = {10.1109/SAMOS.2011.6045491},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {2011 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation},

pages = {404-411},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{The07,

title = {A Performance Analysis Tool for Scenario-Aware Streaming Applications},

author = {B. D. Theelen},

doi = {10.1109/QEST.2007.7},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

booktitle = {Fourth International Conference on the Quantitative Evaluation of Systems (QEST 2007)},

pages = {269-270},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{TGBea06,

title = {A scenario-aware data flow model for combined long-run average and worst-case performance analysis},

author = {B. D. Theelen and M. C. W. Geilen and T. Basten and J. P. M. Voeten and S. V. Gheorghita and S. Stuijk},

doi = {10.1109/MEMCOD.2006.1695924},

year  = {2006},

date = {2006-07-01},

urldate = {2006-07-01},

booktitle = {MEMOCODE},

pages = {185-194},

abstract = {Data flow models are used for specifying and analysing signal processing and streaming applications. However, traditional data flow models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the synchronous data flow model, which uses a stochastic approach to model the order in which scenarios occur. The formally defined operational semantics of a scenario-aware data flow model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using scenario-aware data flow models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GSBC05,

title = {Automatic Scenario Detection for Improved WCET Estimation},

author = {Stefan Valentin Gheorghita and Sander Stuijk and Twan Basten and Henk Corporaal},

url = {http://doi.acm.org/10.1145/1065579.1065610},

doi = {10.1145/1065579.1065610},

isbn = {1-59593-058-2},

year  = {2005},

date = {2005-01-01},

urldate = {2005-01-01},

booktitle = {Proceedings of the 42Nd Annual Design Automation Conference},

pages = {101–104},

publisher = {ACM},

address = {Anaheim, California, USA},

series = {DAC '05},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}