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A Comparison of Multi-Objective Algorithms for the Automatic Design Space Exploration of a Superscalar System
| Content Provider | Semantic Scholar |
|---|---|
| Author | Calborean, Horia Jahr, Ralf Ungerer, Theo Vintan, Lucian |
| Copyright Year | 2012 |
| Abstract | In today’s computer architectures the design spaces are huge, thus making it very difficult to find optimal configurations. One way to cope with this problem is to use Automatic Design Space Exploration (ADSE) techniques. We developed the Framework for Automatic Design Space Exploration (FADSE) which is focused on microarchitectural optimizations. This framework includes several state-of-the art heuristic algorithms. In this paper we selected three of them, NSGA-II and SPEA2 as genetic algorithms as well as SMPSO as a particle swarm optimization, and compared their performance. As test case we optimize the parameters of the Grid ALU Processor (GAP) microarchitecture and then GAP together with the post-link code optimizer GAPtimize. An analysis of the simulation results shows a very good performance of all the three algorithms. SMPSO reveals the fastest convergence speed. A clear winner between NSGA-II and SPEA2 cannot be determined. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.researchgate.net/profile/Lucian_Vintan/publication/264737397_A_Comparison_of_Multi-Objective_Algorithms_for_the_Automatic_Design_Space_Exploration_of_a_Superscalar_System/links/53ed011e0cf23733e8051a42.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Architecture as Topic Arithmetic logic unit Computer architecture Design space exploration Fastest Genetic algorithm Heuristic Mathematical optimization Microarchitecture Multi-objective optimization Particle swarm optimization Program optimization Simulation Superscalar processor Test case Views grid:Find:Pt:Breast:Doc:Mam |
| Content Type | Text |
| Resource Type | Article |
