Abstract
Embedded systems design requires conflicting objectives to be optimized with an appropriate choice of hardware-software parameters. A simulation campaign can guide the design in finding the best trade-offs, but due to the big number of possible configurations, it is often unfeasible to simulate them all. For these reasons, design space exploration algorithms aim at finding near-optimal system configurations by simulating only a subset of them.
In this work, we present PS, a new multiobjective optimization algorithm, and evaluate it in the context of the embedded system design. The basic idea is to recognize interesting regions—that is, regions of the configuration space that provide better configurations with respect to other ones. PS evaluates more configurations in the interesting regions while less thoroughly exploring the rest of the configuration space. After a detailed formal description of the algorithm and the underlying concepts, we show a case study involving the hardware/software exploration of a VLIW architecture. Qualitative and quantitative comparisons of PS against a well-known multiobjective genetic approach demonstrate that while not outperforming it in terms of Pareto dominance, the proposed approach can balance the uniformity and granularity qualities of the solutions found, obtaining more extended Pareto fronts that provide a wider view of the potentiality of the designed device. Therefore, PS represents a further valid choice for the designer when objective constrains allow it.
- Santosh G. Abraham, B. Ramakrishna Rau, and Robert Schreiber. 2000. Fast Design Space Exploration Through Validity and Quality Filtering of Subsystem Designs. Technical Report HPL-2000-98. HP Laboratories, Palo Alto, CA.Google Scholar
- Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, and Davide Patti. 2011. Performance evaluation of efficient multi-objective evolutionary algorithms for design space exploration of embedded computer systems. Applied Soft Computing 11, 1, 382--398. DOI:http://dx.doi.org/10.1016/j.asoc.2009.11.029 Google Scholar
Digital Library
- Giuseppe Ascia, Vincenzo Catania, and Maurizio Palesi. 2002. Tuning methodologies for parameterized systems design. In Proceedings of the International Workshop on System-on-Chip for Real-Time Applications. 71--82.Google Scholar
- Giuseppe Ascia, Vincenzo Catania, Maurizio Palesi, and Davide Patti. 2005. A system-level framework for evaluating area/performance/power trade-offs of VLIW-based embedded systems. In Proceedings of the Asia and South Pacific Design Automation Conference (ASP-DAC’05). 940--943. http://www.scopus.com/inward/record.url?eid=2-s2.0-49749111784&partnerID=40&md5=59dcbd7d5d390c3d69c693f3cdbac7cb. Google Scholar
Digital Library
- Pradnya S. Borkar and Anjali R. Mahajan. 2014. Types and applications of parallel genetic algorithm. International Journal of Advanced Research in Computer Science and Software Engineering 4, 4, 1387--1390.Google Scholar
- Erick Cantú-Paz. 1998. A survey of parallel genetic algorithms. Calculateurs Paralleles 10, 2, 141--171.Google Scholar
- Vincenzo Catania, Andrea Araldo, and Davide Patti. 2014. Complete results of Paramspace tests. Retrieved August 14, 2015, from https://sites.google.com/site/xedivad/home/research/PS_TEST.tgz?attredirects=0&d=1.Google Scholar
- Vincenzo Catania, Maurizio Palesi, and Davide Patti. 2008. Reducing complexity of multiobjective design space exploration in VLIW-based embedded systems. ACM Transactions on Architecture and Code Optimization 5, 2, Article No. 11. DOI:http://dx.doi.org/10.1145/1400112.1400116 Google Scholar
Digital Library
- Carlos A. Coello Coello, David A. Van Veldhuizen, and Gary B. Lamont. 2002. Evolutionary Algorithms for Solving Multi-Objective Problems, Vol. 5. Kluwer Academic.Google Scholar
- Matej Črepinšek, Shih-Hsi Liu, and Marjan Mernik. 2013. Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys 45, 3, 35. Google Scholar
Digital Library
- Michael Dellnitz, Oliver Schütze, and Thorsten Hestermeyer. 2005. Covering Pareto sets by multilevel subdivision techniques. Journal of Optimization Theory and Applications 124, 1, 113--136.Google Scholar
Digital Library
- Michael Dellnitz and Katrin Witting. 2009. Computation of robust Pareto points. International Journal of Computing Science and Mathematics 2, 3, 243--266. Google Scholar
Digital Library
- Agoston E. Eiben and Cornelis A. Schippers. 1998. On evolutionary exploration and exploitation. Fundamenta Informaticae 35, 1, 35--50. Google Scholar
Digital Library
- William Fornaciari, Donatella Sciuto, Cristina Silvano, and Vittorio Zaccaria. 2001. A design framework to efficiently explore energy-delay tradeoffs. In Proceedings of the 9th International Symposium on Hardware/Software Co-Design. 260--265. Google Scholar
Digital Library
- Tony Givargis, Frank Vahid, and Jörg Henkel. 2002. System-level exploration for Pareto-optimal configurations in parameterized system-on-a-chip. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 10, 2, 416--422. Google Scholar
Digital Library
- Daniel Kahneman. 1973. Attention and Effort. Prentice Hall.Google Scholar
- Vinod Kathail, Michael S. Schlansker, and B. Ramakrishna Rau. 2000. HPL-PD Architecture Specification: Version 1.0. Technical Report HPL-93-80. Compiler and Architecture Research HP Laboratories, Palo Alto, CA.Google Scholar
- Joshua Knowles, Lothar Thiele, and Eckart Zitzler. 2006. A Tutorial on the Performance Assessment of Stochastive Multiobjective Optimizers. Technical Report 214. Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland.Google Scholar
- Vilfredo Pareto. 1896. Cours D’Economie Politique. Vols. I and II. F. Rouge, Lausanne, Switzerland.Google Scholar
- Davide Patti, Maurizio Palesi, and Vincenzo Catania. 2014. Merging compilation and microarchitectural configuration spaces for performance/power optimization in VLIW-based systems. In Modern Trends and Techniques in Computer Science. Advances in Intelligent Systems and Computing, Vol. 285. Springer, 203--212. DOI:http://dx.doi.org/10.1007/978-3-319-06740-7_18Google Scholar
- George Sperling and Erich Weichselgartner. 1995. Episodic theory of the dynamics of spatial attention. Psychological Review 102, 3, 503--532.Google Scholar
Cross Ref
- Trimaran. 2010. An Infrastructure for Research in Instruction-Level Parallelism. Available at http://www.trimaran.org/.Google Scholar
- Thomas Weise, Raymond Chiong, and Ke Tang. 2012. Evolutionary optimization: Pitfalls and booby traps. Journal of Computer Science and Technology 27, 5, 907--936.Google Scholar
Cross Ref
- WSTS. 2013. World Semiconductor Trade Statistics Bluebook. Available at http://www.wsts.org/.Google Scholar
- Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 2, 173--195. Google Scholar
Digital Library
- Eckart Zitzler, Marco Laumanns, and Lothar Thiele. 2001. SPEA2: Improving the performance of the strength Pareto evolutionary algorithm. In Proceedings of EUROGEN 2001: Evolutionary Methods for Design, Optimization, and Control with Applications to Industrial Problems. 95--100.Google Scholar
- Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M. Fonseca, and Viviane Grunert da Fonseca. 2003. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7, 2, 117--132. Google Scholar
Digital Library
Index Terms
Parameter Space Representation of Pareto Front to Explore Hardware-Software Dependencies
Recommendations
Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front
The optimal solutions of a multiobjective optimization problem correspond to a nondominated front that is characterized by a tradeoff between objectives. A knee region in this Pareto-optimal front, which is visually a convex bulge in the front, is ...
Two-Sided Pareto Front Approximations
A new approach to derive Pareto front approximations with evolutionary computations is proposed here.
At present, evolutionary multiobjective optimization algorithms derive a discrete approximation of the Pareto front (the set of objective maps of ...
Image thresholding based on Pareto multiobjective optimization
A new image thresholding method based on multiobjective optimization following the Pareto approach is presented. This method allows to optimize several segmentation criteria simultaneously, in order to improve the quality of the segmentation. To obtain ...






Comments