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TUTCRIS

Reconfigurable computing for future vision-capable devices

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings - 2015 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2015
KustantajaInstitute of Electrical and Electronics Engineers Inc.
Sivut34-41
Sivumäärä8
ISBN (elektroninen)9781467373111
DOI - pysyväislinkit
TilaJulkaistu - 22 joulukuuta 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma15th International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2015 - Samos, Kreikka
Kesto: 20 heinäkuuta 201523 heinäkuuta 2015

Conference

Conference15th International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, SAMOS 2015
MaaKreikka
KaupunkiSamos
Ajanjakso20/07/1523/07/15

Tiivistelmä

Mobile devices have been identified as promising platforms for interactive vision-based applications. However, this type of applications still pose significant challenges in terms of latency, throughput and energy-efficiency. In this context, the integration of reconfigurable architectures on mobile devices allows dynamic reconfiguration to match the computation and data flow of interactive applications, demonstrating significant performance benefits compared to general purpose architectures. This paper presents concepts laying on platform level adaptability, exploring the acceleration of vision-based interactive applications through the utilization of three reconfigurable architectures: A low-power EnCore processor with a Configurable Flow Accelerator co-processor, a hybrid reconfigurable SIMD/MIMD platform and Transport-Triggered Architecture-based processors. The architectures are evaluated and compared with current processors, analyzing their advantages and weaknesses in terms of performance and energy-efficiency when implementing highly interactive vision-based applications. The results show that the inclusion of reconfigurable platforms on mobile devices can enable the computation of several computationally heavy tasks with high performance and small energy consumption while providing enough flexibility.