@conference {17772,
	title = {A Security-enhanced Design Methodology For Embedded Systems},
	booktitle = { ICETE SECRYPT 2013},
	year = {2013},
	month = {07/2013},
	publisher = {ICETE},
	organization = {ICETE},
	address = {Reykjavik, Iceland},
	keywords = {design methodology, design space exploration, embedded systems, metric, security, security metric},
	author = {Ferrante, Alberto and Milosevic, Jelena and Janjusevic, Marija}
}
@article {156.MaPaSiZa12.TCAD,
	title = {OSCAR: an Optimization Methodology Exploiting Spatial Correlation in Multi-core Design Space},
	journal = {IEEE Transactions on Computer-Aided Design},
	volume = {21},
	number = {-},
	issue = {5},
	year = {2012},
	month = {05/2012},
	pages = {740-753},
	publisher = {IEEE},
	abstract = {This paper presents OSCAR, an Optimization methodology exploiting Spatial CorrelAtion of multi-coRe design space. The paper builds upon the observation that power consumption and performance metrics of spatially close design configurations (or points) are statistically correlated. We propose to exploit the correlation by using a Response Surface Model (RSM), i.e., a closed-form expression suitable for predicting the quality of non-simulated design points. This model is useful during the design space exploration (DSE) phase to quickly converge to the Pareto set of the multi-objective problem without executing lengthy simulations. We compare the proposed heuristic with state-of-the-art approaches (conventional, RSM-based and structured DOEs). Experimental results show that OSCAR is a faster heuristic with respect to state of the art techniques such as Response-Surface Pareto Iterative Refinement - ReSPIR and Nondominated Sorting Genetic Algorithm - NSGA-II. Reported results also show that OSCAR can significantly improve structured DOE approaches by slightly increasing the number of experiments.},
	keywords = {chip multi processor, correlation based design, design space exploration, multi-core, multi-objective optimization, OSCAR},
	author = {Mariani, Giovanni and Palermo, Gianluca and Silvano, Cristina and Zaccaria, Vittorio}
}
@conference {126.MaPaZaBrJoSi11,
	title = {A Correlation-based Design Space Exploration Methodology for Multi-Processor Systems-on-Chip},
	booktitle = {Proceedings of DAC 2010: Design Automation Conference},
	year = {2010},
	month = {June},
	pages = {120{\textendash}125},
	address = {Anheim, CA, USA},
	abstract = {Given the increasing complexity of multi-processor systems-on-chip, a wide range of parameters must be tuned to find the best trade-offs in terms of the selected system figures of merit (such as energy, delay and area). This optimization phase is called Design Space Exploration (DSE) consisting of a Multi-Objective Optimization (MOO) problem. In this paper, we propose an iterative design space exploration methodology exploiting the statistical properties of known system configurations to infer, by means of a correlation-based analysis, the next design points to be analyzed with low-level simulations. In fact, the knowledge of few design points is used to predict the expected improvement of unknown configurations. We show that the correlation of the configurations within the multi-processor design space can be modeled successfully with analytical functions and, thus, speed up the overall exploration phase. This makes the proposed methodology a model-assisted heuristic that, for the first time, exploits the correlation about architectural configurations to converge to the solution of the multi-objective problem.},
	keywords = {design space exploration, kriging, multiprocessor system-on-chip (MPSoC), response surface},
	author = {Mariani, Giovanni and Palermo, Gianluca and Zaccaria, Vittorio and Brankovic, Aleksandar and Jovic, Jovana and Silvano, Cristina}
}
@conference {98.MaPaSiZa309,
	title = {A Design Space Exploration Methodology Supporting Run-Time Resource Management for Multi-Processors System on-Chip},
	booktitle = {Proceedings IEEE SASP{\textquoteright}09 - Symposium on Application Specific Processors},
	year = {2009},
	month = {July},
	address = {San Francisco, CA, USA},
	abstract = {Application Specific multi-processor Systems-on-chip are currently designed by using platform-based synthesis techniques. In this approach, a wide range of platform parameters are tuned either at design-time or at run-time, to provide the best trade-offs in terms of the selected system figures of merit (such as power and throughput) for a dynamic application-specific workload. Among the design-time (hardware) configurable parameters we can find the memory sub-system configuration (e.g. cache size and associativity) and other architectural parameters such as the instruction-level parallelism of the system processors. Among the run-time (software) configurable parameters we can find the overall degree of task-level parallelism associated with each application running on the chip. Typically, while the design-time exploration is performed in the early development stages for a set of static parameters, the tuning of the run-time parameters is performed dynamically by a run-time management software module after the system has been deployed. In this paper, we introduce a methodology for identifying a hardware configuration which is robust with respect to the variable workload scenario introduced by the run-time management. Moreover, the proposed methodology is aimed at providing useful information about the optimal software operating points of the applications in terms of task-level parallelism. The proposed methodology is based on the NSGA-II evolutionary heuristic algorithm assisted by an Artificial Neural Network (ANN). We then introduce a run-time management policy which is able to exploit the above information to maximize the performance of the system under power budget constraints. Experimental results show that the proposed technique is able to reduce the overall design space exploration time yet providing a near-optimal solution, in terms of hardware parameters, to enable an innovative and efficient run-time anagement policy.},
	keywords = {artificial neural network, design space exploration, meta-model assisted optimization, multi-objective optimization, multiprocessor system-on-chip (MPSoC), run-time resource management},
	doi = {http://dx.doi.org/10.1109/SASP.2009.5226331},
	author = {Mariani, Giovanni and Palermo, Gianluca and Silvano, Cristina and Zaccaria, Vittorio}
}
@conference {77.MaToFi08,
	title = {Design Space Exploration of PISA Architecture For ONU Auto-discovery Process},
	booktitle = {proceedings of 6th International Conference of Electrical Engineering (ICEENG)},
	year = {2009},
	month = {May 27-29},
	address = {Cairo, Egypt},
	abstract = {The goal of the paper is to optimize the PISA architecture for the ONU Auto-discovery process. This Auto-discovery process has been written in C language following the IEEE 802.3ah MPCP standard. Using SimpleScalar [3] simulation tool, the architecture profile is evaluated in order to decide the range of the design exploration. Then, using Wattch [1] and CACTI [2] simulation tools the CPI, average power consumed and cache area are calculated for each design point, the cost function is defined and evaluated for each design point using greedy strategy. The Auto-discovery process has been written in VHDL and using Synopys power compiler [4] the power consumption has been calculated and then we compared between the VHDL implementation and the PISA architecture from the power consumption point of view.},
	keywords = {design space exploration},
	doi = {http://dx.doi.org/10.1109/ICNM.2009.4907186},
	author = {Mady, Alie El-Din and Tonini, Andrea and Finardi, Davide}
}
@conference {99.MaPaSiZa09,
	title = {Multiprocessor System-on-Chip Design Space Exploration based on Multi-level Modeling Techniques},
	booktitle = {Proceedings of IEEE IC-SAMOS{\textquoteright}09 - International Conference on Embedded Computer Systems: Architectures, MOdeling, and Simulation},
	year = {2009},
	month = {July},
	address = {Samos, Greece},
	abstract = {Multi-processor Systems-on-chip are currently designed by using platform-based synthesis techniques. In this approach, a wide range of platform parameters are tuned to find the best trade-offs in terms of the selected system figures of erit (such as energy, delay and area). This optimization phase is called Design Space Exploration (DSE) and it generally consists of a Multi-Objective Optimization (MOO) problem. The design space of a Multi-processor architecture is too large to be evaluated comprehensively. So far, several heuristic techniques have been proposed to address the MOO problem, but they are haracterized by low efficiency to identify the Pareto set. In this paper we propose a methodology for heuristic platform based design based on evolutionary algorithms and multi-level simulation techniques. In particular, we extend the NSGA-II with an approximate neural network meta-model for multi-processor architectures in order to replace expensive platform simulations with fast meta-model evaluation. The model accuracy and efficiency is improved by exploiting high-level platform simulation techniques. High-level simulation allows us to reduce the overall complexity of the neural network and improving its prediction power. Experimental results show that the proposed techniques is able to reduce the number of simulations needed for the optimization without decreasing the quality of the obtained Pareto set. Results are compared with state of the art echniques to demonstrate that optimization time due to simulation can be sped up by adopting multi-level simulation techniques.},
	keywords = {artificial neural network, design space exploration, genetic algorithm, multi level modelling, multiprocessor system-on-chip (MPSoC), platform-based design},
	doi = {http://dx.doi.org/10.1109/ICSAMOS.2009.5289222},
	author = {Mariani, Giovanni and Palermo, Gianluca and Silvano, Cristina and Zaccaria, Vittorio}
}
@conference {90.MaPaZaSi08,
	title = {An Efficient Design Space Exploration Methodology for Multi-Cluster VLIW Architectures based on Artificial Neural Networks},
	booktitle = {Proc. IFIP International Conference on Very Large Scale Integration VLSI - SoC 2008},
	year = {2008},
	month = {October 13-15},
	address = {Rhodes Island, Greece},
	abstract = {Multi-Cluster Very Long Instruction Word (VLIW) architectures are currently designed by using platform-based synthesis techniques. In these approaches, a wide range of platform parameters is tuned to find the best trade-offs in terms of the selected figures of merit (such as energy, delay and area). This optimization phase is called Design Space Exploration (DSE) and it generally consists of a Multi-Objective Optimization (MOO) problem. The design space for a Multi-Cluster architecture is too large to be evaluated comprehensively. So far, several heuristic techniques have been proposed to address the MOO problem, but they are characterized by low efficiency to identify the Pareto front. In this paper, we propose an efficient DSE methodology leveraging neural networks. In particular, an initial design-of-experiments phase is used for generating a coarse view of the target design space; neural networks are then trained and used to refine the exploration, by identifying efficiently the Pareto points of the design space. This process is iteratively repeated until the target criterion (convergence of the Pareto coverage) is satisfied. A set of experimental results are reported to trade-off accuracy and efficiency of the proposed techniques with actual workloads.},
	keywords = {design space exploration, multi-objective optimization, neural networks, response surface, system-on-chip (SoC), very long instruction words (VLIW)},
	author = {Mariani, Giovanni and Palermo, Gianluca and Zaccaria, Vittorio and Silvano, Cristina}
}
@conference {44.SivaPrev2006,
	title = {Bridging the Gap between SysML and Design Space Exploration},
	booktitle = {FDL{\textquoteright}06 Proceedings},
	year = {2006},
	month = {September 19-22},
	pages = {389-394},
	address = {Darmstadt, Germany},
	abstract = {In the last few years the embedded systems design discipline required new design methodologies and new specification languages to support system engineers in developing heterogeneous systems where hardware and software are combined. One of the emerging modeling languages for system designers is the UML-based language called Systems Modeling Language (SysML). One of the most important tasks to be addressed early in the system design phase is the Design Space Exploration (DSE). DSE helps designers in discovering the optimal solutions among all possible combinations after mapping functional to architectural specifications. This paper describes an approach on how to use SysML for a DSE analysis within a system design phase.},
	keywords = {design space exploration, HW/SW co-design, modeling languages, systems modeling language (SysML)},
	author = {Sivakumar, Ganesan and Prevostini, Mauro}
}
@conference {28.1049903,
	title = {Application-Driven Optimization of VLIW Architectures: A Hardware-Software Approach},
	booktitle = {RTAS {\textquoteright}05: Proceedings of the 11th IEEE Real Time on Embedded Technology and Applications Symposium},
	year = {2005},
	month = {03/2005},
	pages = {128{\textendash}137},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Washington, DC, USA},
	abstract = {A large number of embedded multimedia applications are characterized by high instruction-level parallelism (ILP) expecially in the most critical internal loop bodies. Very Large Instruction Word (VLIW) architectures Application Specific Instruction Set Processors (ASIP) are best suited to exploit such parallelism. Fast design space exploration and optimization of VLIW architecture to a specific application target is increasingly becoming the crucial factor to achieve higher efficiency designs in a relatively small amount of time. In this paper we propose an example of VLIW architecture application driven optimization using the VEX (VLIW Example) system. A typical image processing application, the Imaging Pipeline, has been chosen as an example.},
	keywords = {design space exploration, embedded systems, HW/SW co-design, HW/SW partitioning, system level design, very long instruction words (VLIW)},
	isbn = {0-7695-2302-1},
	doi = {http://dx.doi.org/10.1109/RTAS.2005.9},
	author = {Ferrante, Alberto and Piscopo, Giuseppe and Scaldaferri, Stefano}
}
@conference {9.SaSaSciSiZaZa2003,
	title = {A Methodology for efficient architectural exploration of energy-delay trade-offs for embedded systems},
	booktitle = {SAC 2003},
	year = {2003},
	month = {March},
	pages = {672-678},
	address = {Melbourne},
	abstract = {The main goal of this paper is to identify the best architecture of an embedded system by considering at the same time energy and delay, avoiding the comprehensive analysis of the architectural design space. We adopt the Energy-Delay Product (EDP) as the evaluation metric to compare the alternative architectures of the target system. The paper analyzes an extended adaptive random search algorithm (ADGREED) to efficiently explore the architectural design space. The ADGREED algorithm is a pseudo-random optimization algorithm that combines the best potentialities of the adaptive random search (ADRAS) and the Greedy deterministic algorithm. The analysis has been carried out through the architectural optimization of the memory subsystem of a real-word embedded system executing the set of Mediabench benchmarks for multimedia applications. The reported experimental results have shown a reduction up to one order of magnitude of the number of design alternatives analyzed during the exploration phase, while maintaining very high accuracy.},
	keywords = {design space exploration, embedded systems},
	doi = {http://dx.doi.org/10.1145/952532.952664},
	author = {Salvemini, Lorenzo and Sami, Mariagiovanna and Sciuto, Donatella and Silvano, Cristina and Zaccaria, Vittorio and Zafalon, Roberto}
}
