Synthesis of self-adaptable energy aware software for heterogeneous multicore embedded systems
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- @Article{DENIZIAK:2021:MR,
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author = "Stanislaw Deniziak and Leszek Ciopinski",
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title = "Synthesis of self-adaptable energy aware software for
heterogeneous multicore embedded systems",
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journal = "Microelectronics Reliability",
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volume = "123",
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pages = "114184",
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year = "2021",
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ISSN = "0026-2714",
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DOI = "doi:10.1016/j.microrel.2021.114184",
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URL = "https://www.sciencedirect.com/science/article/pii/S0026271421001505",
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keywords = "genetic algorithms, genetic programming,
Self-adaptivity, Embedded system, Developmental genetic
programing, Multicore system",
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abstract = "Contemporary embedded systems work in changing
environments, some features (e.g., execution time,
power consumption) of the system are often not
completely predictable. Therefore, for systems with
strong constraints, a worst-case design is applied. We
observed that by enabling the self-adaptivity we may
obtain highly optimized systems still guaranteeing the
high quality of service. This paper presents a method
of synthesis of real-time software for self-adaptive
multicore systems. The method assumes that the system
specification is given as a task graph. Then, the tasks
are scheduled on a multicore architecture consisting of
low-power and high-performance cores. We apply the
developmental genetic programming to generate the
self-adaptive scheduler and the initial schedule. The
initial schedule is optimized, taking into
consideration the power consumption, the real-time
constraints as well as the self-adaptivity. The
scheduler modifies the schedule during the system
execution, whenever execution time of the recently
finished task occurs other than assumed during the
initial scheduling. We propose two models of
self-adaptivity: self-optimization of power consumption
and self-adaptivity of real-time scheduling. We present
some experimental results for standard benchmarks,
showing the advantages of our method in comparison with
the worst case design used in existing approaches",
- }
Genetic Programming entries for
Stanislaw Deniziak
Leszek Ciopinski
Citations