Multi-processor system-on-chip Design Space Exploration based on multi-level modeling techniques

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 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.

[1]  Vincenzo Catania,et al.  Efficient design space exploration for application specific systems-on-a-chip , 2007, J. Syst. Archit..

[2]  Ching-Lai Hwang,et al.  Methods for Multiple Objective Decision Making , 1979 .

[3]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[4]  Bernhard Sendhoff,et al.  A framework for evolutionary optimization with approximate fitness functions , 2002, IEEE Trans. Evol. Comput..

[5]  K. Keutzer,et al.  System-level design: orthogonalization of concerns andplatform-based design , 2000, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[6]  Margaret Martonosi,et al.  Wattch: a framework for architectural-level power analysis and optimizations , 2000, Proceedings of 27th International Symposium on Computer Architecture (IEEE Cat. No.RS00201).

[7]  Anoop Gupta,et al.  The SPLASH-2 programs: characterization and methodological considerations , 1995, ISCA.

[8]  Cristina Silvano,et al.  An Efficient Design Space Exploration Methodology for Multi-Cluster VLIW Architectures based on Artificial Neural Networks , 2008 .

[9]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[10]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[11]  Vittorio Zaccaria,et al.  An Efficient Design Space Exploration Methodology for On-Chip Multiprocessors Subject to Application-Specific Constraints , 2008, 2008 Symposium on Application Specific Processors.

[12]  Adam Blum,et al.  Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems , 1992 .

[13]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[14]  Vittorio Zaccaria,et al.  Multi-objective design space exploration of embedded systems , 2003, J. Embed. Comput..

[15]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[16]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[17]  Cristina Silvano,et al.  Decision-theoretic exploration of multiProcessor platforms , 2006, Proceedings of the 4th International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS '06).

[18]  Yaochu Jin,et al.  Managing approximate models in evolutionary aerodynamic design optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[19]  Norman P. Jouppi,et al.  CACTI: an enhanced cache access and cycle time model , 1996, IEEE J. Solid State Circuits.

[20]  Maumita Bhattacharya,et al.  Meta Model Based EA for Complex Optimization , 2008 .

[21]  Joshua D. Knowles,et al.  ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.

[22]  Piotr Czyzżak,et al.  Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization , 1998 .