Analysis of Inner-Loop Mapping onto Coarse-Grained Reconfigurable Architectures Using Hybrid Particle Swarm Optimization
Theodore S. Norvell and
Additional contact information
Rani Gnanaolivu: Memorial University of Newfoundland, Canada
Theodore S. Norvell: Memorial University of Newfoundland, Canada
Ramachandran Venkatesan: Memorial University of Newfoundland, Canada
International Journal of Organizational and Collective Intelligence (IJOCI), 2011, vol. 2, issue 2, 17-35
Coarse-Grained Reconfigurable Architectures (CGRAs) have gained currency in recent years due to their abundant parallelism and flexibility. To utilize the parallelism found in CGRAs, this paper proposes a fast and efficient Modulo-Constrained Hybrid Particle Swarm Optimization (MCHPSO) scheduling algorithm to exploit loop-level parallelism in applications. This paper shows that Particle Swarm Optimization (PSO) is capable of software pipelining loops by overlapping placement, scheduling and routing of successive loop iterations and executing them in parallel. The proposed algorithm has been experimentally validated on various DSP benchmarks under two different architecture configurations. These experiments indicate that the proposed MCHPSO algorithm can find schedules with small initiation intervals within a reasonable amount of time. The MCHPSO scheduling algorithm was analyzed with different topologies and Functional Unit (FU) configurations. The authors have tested the parallelizability of the algorithm and found that it exhibits a nearly linear speedup on a multi-core CPU.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/joci.2011040102 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:joci00:v:2:y:2011:i:2:p:17-35
Access Statistics for this article
More articles in International Journal of Organizational and Collective Intelligence (IJOCI) from IGI Global
Bibliographic data for series maintained by Journal Editor ().