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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/7352
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dc.contributor.authorWang, Haomiao-
dc.contributor.authorPrabu, T.-
dc.contributor.authorSinnen, Oliver-
dc.date.accessioned2019-11-18T05:57:34Z-
dc.date.available2019-11-18T05:57:34Z-
dc.date.issued2018-03-
dc.identifier.citationIEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018, Vol.27, p624-636en_US
dc.identifier.issn1063-8210-
dc.identifier.issn1557-9999(Online)-
dc.identifier.urihttp://hdl.handle.net/2289/7352-
dc.descriptionRestricted Accessen_US
dc.description.abstractThe Square Kilometer Array, which will be the world's largest radio telescope, will enhance and boost a large number of science projects, including the search for pulsars. The frequency-domain acceleration search is an efficient approach to search for binary pulsars. A significant part of it is the harmonic-summing module, which is the research subject of this paper. Most of the operations in the harmonic-summing module are relatively cheap operations for field-programmable gate arrays (FPGAs). The main challenge is the large number of point accesses to off-chip memory, which are not consecutive but irregular. Having the harmonic summing on the FPGA will avoid off-board communication with other pulsar search modules, which could destroy other acceleration benefits. Two types of harmonic-summing approaches are investigated in this paper: (1) storing intermediate data in off-chip memory and (2) processing the input signals directly without storing. For the second type, two approaches of caching data are proposed and evaluated: (1) preloading points that are frequently touched and (2) preloading all necessary points that are used to generate a chunk of output points. Open Computing Language (OpenCL) is adopted to implement the proposed approaches. In an extensive experimental evaluation, the same OpenCL kernel codes are evaluated on FPGA boards and GPU cards. Regarding the proposed preloading methods, preloading all necessary points method while reordering the input signals is faster than all the other methods. While in raw performance, a single-FPGA board cannot compete with a GPU. Regarding energy dissipation, GPU costs up to 2.6× times more energy than that of FPGAs in executing the same NDRange kernels.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urihttps://doi.org/10.1109/TVLSI.2018.2882238en_US
dc.rights2018, IEEEen_US
dc.subjectField-programmable gate arrays (FPGAs)en_US
dc.subjectharmonic summingen_US
dc.subjectirregular memory access optimizationen_US
dc.subjectOpen Computing Language (OpenCL).en_US
dc.titleHarmonic-Summing Module of SKA on FPGA—Optimizing the Irregular Memory Accessesen_US
dc.typeArticleen_US
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