@conference {157.mariani2012parma,
	title = {Evaluating Run-time Resource Management Policies for Multi-core Embedded Platforms with the EMME Evaluation Framework},
	booktitle = {Workshop on Parallel Programming and Run-time Management Techniques for Many-core Architectures},
	year = {2012},
	month = {February},
	keywords = {EMME, multi-core, run-time resource management, simulation},
	author = {Mariani, Giovanni and Palermo, Gianluca and Zaccaria, Vittorio and Silvano, Cristina}
}
@conference {114.MaYkZhZhLa10,
	title = {An Efficient Run-Time Management Methodology for Stereo Matching Application},
	booktitle = {2PARMA: Proceedings of the Workshop on Parallel Programming and Run-time Management Techniques for Many-core Architectures},
	year = {2010},
	month = {February},
	address = {Hannover, Germany},
	abstract = {This paper presents a methodology for Run-Time Management (RTM) of algorithmic parameters. The RTM is able to trade-off the algorithm output quality and the execution time. Thus, once a requirement in terms of maximum execution time is set, the RTM dynamically tunes the parameters in order to maximize the output quality while respecting the given requirement. The run-time decision making relies on design-time modeling techniques able to characterize key relations between algorithm parameters, execution time and output quality. Models generated during the design-time analysis are accurate enough to drive the RTM in its decision making while enough generic to model application behaviors over datasets which were not included at design-time. In this paper the methodology is applied on the Stereo Matching application, a computational intensive artificial vision application aimed at inferring object depths using two or more cameras. Experimental results prove the effectiveness of the methodology which is able to identify high quality solutions respecting required deadline while introducing negligible overhead.},
	author = {Mariani, Giovanni and Ykman-Couvreur, Chantal and Zhang, Ke and Zhang, Lu and Lafruit, Gauthier}
}
@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}
}
