|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||Area-Energy Aware Dataflow Optimisation of Visual Tracking Systems|
|Citation:||Garcia P, Bhowmik D, Wallace A, Stewart R & Michaelson G (2018) Area-Energy Aware Dataflow Optimisation of Visual Tracking Systems. In: Voros N, Huebner M, Keramidas G, Goehringer D, Antonopoulos C & Diniz P (eds.) Applied Reconfigurable Computing. Architectures, Tools, and Applications. Lecture Notes in Computer Science, 10824. ARC 2018: International Symposium on Applied Reconfigurable Computing, Santorini, Greece, 02.05.2018-04.05.2018. Cham, Switzerland: Springer International Publishing, pp. 523-536. https://doi.org/10.1007/978-3-319-78890-6_42|
|Series/Report no.:||Lecture Notes in Computer Science, 10824|
|Conference Name:||ARC 2018: International Symposium on Applied Reconfigurable Computing|
|Conference Dates:||2018-05-02 - 2018-05-04|
|Conference Location:||Santorini, Greece|
|Abstract:||This paper presents an orderly dataflow-optimisation approach suitable for area-energy aware computer vision applications on FPGAs. Vision systems are increasingly being deployed in power constrained scenarios, where the dataflow model of computation has become popular for describing complex algorithms. Dataflow model allows processing datapaths comprised of several independent and well defined computations. However, compilers are often unsuccessful in identifying domain-specific optimisation opportunities resulting in wasted resources and power consumption. We present a methodology for the optimisation of dataflow networks, according to patterns often found in computer vision systems, focusing on identifying optimisations which are not discovered automatically by an optimising compiler. Code transformation using profiling and refactoring provides opportunities to optimise the design, targeting FPGA implementations and focusing on area and power abatement. Our refactoring methodology, applying transformations to a complex algorithm for visual tracking resulted in significant reduction in power consumption and resource usage.|
|Status:||AM - Accepted Manuscript|
|Rights:||This is a post-peer-review, pre-copyedit version of a paper published in Voros N, Huebner M, Keramidas G, Goehringer D, Antonopoulos C & Diniz P (eds.) Applied Reconfigurable Computing. Architectures, Tools, and Applications. Lecture Notes in Computer Science, 10824. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-78890-6_42|
|ARC2018-Paper-19.pdf||Fulltext - Accepted Version||790.58 kB||Adobe PDF||View/Open|
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