Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/58989
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dc.contributor.authorChoo, Yong Cheng
dc.date.accessioned2014-04-17T09:12:46Z
dc.date.available2014-04-17T09:12:46Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/58989
dc.description.abstractWith an increasing volume and velocity of data in today's applications in many industries such as financial, healthcare and weather predictions, new acceleration tools and methods are coming up rapidly. There is an insatiable demand for more computing power while also achieving accurate and fast results. High Performance Computing is the backbone of these new accelerators. I will be surveying existing techniques on acceleration for complex calculations in the computational finance industry with the 3 platforms CPU, GPU and FPGA. Thus, I will be reporting speedups and conduct detailed experiments to quantify benefits of acceleration in comparison of the 3 platforms. At the end of the day, the deliverable is to use utilize Maxeler framework to optimise and choose the best fit 3D Convolution Array Blocking size.en_US
dc.format.extent41 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computer systems organization::Performance of systemsen_US
dc.titlePreliminary experiments with Maxeler frameworken_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.organizationAdvanced Digital Sciences Centeren_US
dc.contributor.researchCentre for High Performance Embedded Systemsen_US
dc.contributor.supervisor2Nachiket Kapreen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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