@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 {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 {6.BoSaSciSiZaZa2002,
	title = {Energy Estimation and Optimization of Embedded VLIW Processors based on Instruction Clustering},
	booktitle = {39th Design Automation Conference},
	year = {2002},
	month = {June 10-14},
	pages = {886-891},
	address = {New Orleans},
	abstract = {Aim of this paper is to propose a methodology for the definition of an instruction-level energy estimation framework for VLIW (Very Long Instruction Word) processors. The power modeling methodology is the key issue to define an effective energy-aware software optimisation strategy for state-of-the-art ILP (Instruction Level Parallelism) processors. The methodology is based on an energy model for VLIW processors that exploits instruction clustering to achieve an efficient and fine grained energy estimation. The approach aims at reducing the complexity of the characterization problem for VLIW processors from exponential, with respect to the number of parallel operations in the same very long instruction, to quadratic, with respect to the number of instruction clusters. Furthermore, the paper proposes a spatial scheduling algorithm based on a low-power reordering of the parallel operations within the same long instruction. Experimental results have been carried out on the Lx processor, a 4-issue VLIW core jointly designed by HPLabs and STMicroelectronics. The results have shown an average error of 1:9\% between the cluster-based estimation model and the reference design, with a standard deviation of 5:8\%. For the Lx architecture, the spatial instruction scheduling algorithm provides an average energy saving of 12\%.},
	keywords = {power estimation, VLIW architectures},
	doi = {http://dx.doi.org/10.1109/DAC.2002.1012747},
	author = {Bona, Andrea and Sami, Mariagiovanna and Sciuto, Donatella and Silvano, Cristina and Zaccaria, Vittorio and Zafalon, Roberto}
}
