Optimizing energy and throughput for MPSoCs: an integer particle swarm optimization approach

Murtza, Shahid Ali; Ahmad, Ayaz; Qadri, Muhammad Yasir; Qadri, Nadia N.; Ahmed, Jameel

doi:10.1007/s00607-017-0574-5

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Since the time complexity of ILP-based algorithms does not allow them to scale well to larger problem sizes, meta-heuristic algorithms, such as evolutionary and swarm algorithms, are more commonly used today [ 22 ]. They do not make assumptions on the objective functions, and because of that are more flexible, efficient and can find near-optimal solutions in a considerably shorter time [ 16 , 23 , 24 , 25 , 26 , 27 ]. Typically, these algorithms execute many unguided and independent searches from different starting points to increase the chance of reaching global optima.…”

Section: Related Workmentioning

confidence: 99%

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms

Frid

Sruk

Jakobović

2022

Sensors

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Heterogeneous multiprocessor platforms are the foundation of systems that require high computational power combined with low energy consumption, like the IoT and mobile robotics. In this paper, we present five new algorithms for the design space exploration of platforms with elements grouped in clusters with very few connections in between, while these platforms have favorable electric properties and lower production costs, the limited interconnectivity and inability of heterogeneous platform elements to execute all types of tasks, significantly decrease the chance of finding a feasible mapping of application to the platform. We base the new algorithms on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) meta-heuristic and the previously published SDSE mapping algorithm designed for fully interconnected multiprocessor platforms. With the aim to improve the chance of finding feasible solutions for sparsely connected platforms, we have modified the parts of the search process concerning the penalization of infeasible solutions, chromosome decoding, and mapping strategy. Due to the lack of adequate existing benchmarks, we propose our own synthetic benchmark with multiple application and platform models, which we believe can be easily extended and reused by other researchers for further studying this type of platform. The experiments show that four proposed algorithms can find feasible solutions in 100% of test cases for platforms with dedicated clusters. In the case of tile-like platforms, the same four algorithms show an average success rate of 60%, with one algorithm going up to 84%.

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