A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Execution Frequency and Energy Optimization for DVFS-enabled, Near-threshold Processors
Tekijät: Mäkikyrö Sofia, Tuoriniemi Samuli, Anttila Risto, Koskinen Lauri
Toimittaja: IEEE
Konferenssin vakiintunut nimi: International Conference on Advanced Computer Information Technologies (ACIT)
Julkaisuvuosi: 2020
Kokoomateoksen nimi: 2020 10th International Conference on Advanced Computer Information Technologies (ACIT'2020)
Tietokannassa oleva lehden nimi: 2020 10TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER INFORMATION TECHNOLOGIES (ACIT)
Aloitussivu: 518
Lopetussivu: 522
Sivujen määrä: 5
ISBN: 978-1-7281-6759-6
eISBN: 978-1-7281-6760-2
DOI: https://doi.org/10.1109/ACIT49673.2020.9208896
Tiivistelmä
Shown here is an analysis algorithm targeted for near-threshold ultra-wide Dynamic Voltage and Frequency Scaling (UW-DVFS) embedded systems. The algorithm defines execution frequencies based on the software execution trace. Task execution profiling and constraints are used to form a linear optimization problem. Execution frequencies are defined based on the optimization result, the DVFS scaling factors. The system's DVFS overhead is measured and included in the model. The analyzed code is tested on a near-threshold ARM Cortex-M3 core with integrated PLL and power management. In this case, over 49% energy savings can be achieved for industry-standard speech-recognition software without any penalty in application throughput.
Shown here is an analysis algorithm targeted for near-threshold ultra-wide Dynamic Voltage and Frequency Scaling (UW-DVFS) embedded systems. The algorithm defines execution frequencies based on the software execution trace. Task execution profiling and constraints are used to form a linear optimization problem. Execution frequencies are defined based on the optimization result, the DVFS scaling factors. The system's DVFS overhead is measured and included in the model. The analyzed code is tested on a near-threshold ARM Cortex-M3 core with integrated PLL and power management. In this case, over 49% energy savings can be achieved for industry-standard speech-recognition software without any penalty in application throughput.
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