Vis enkel innførsel

dc.contributor.advisorMorrison, Donn
dc.contributor.authorLam, Julian
dc.date.accessioned2016-09-19T14:01:03Z
dc.date.available2016-09-19T14:01:03Z
dc.date.created2016-06-15
dc.date.issued2016
dc.identifierntnudaim:15514
dc.identifier.urihttp://hdl.handle.net/11250/2408422
dc.description.abstractInformation diffusion is where a message or data is passed from vertex to vertex in a network via edges. Information diffusion is often used for simulations in network research because it estimates how information propagates through a network. A set of vertices that in initial state contains the data is known as the \textit{seed nodes}. The diffusion starts at the seed nodes and propagates over the network. Finding the optimal seed nodes is a crucial part of information diffusion. The seed nodes are the optimal targets to start passing messages during a disaster scenario, vaccinate to prevent the spreading of a disease or even targets for viral marketing. Finding these seed nodes is an NP-hard problem and require a significant amount of computation. One common used model in information diffusion is the \textit{independent cascade model} (ICM). ICM can be performed as a \textit{sparse matrix-vector multiplication} (SPMV). By using a application specific hardware accelerator, we can accelerate the diffusion process and consequently, the seed selection. Recently, \textit{high-level synthesis} (HLS) have been getting more attention. HLS transform high-level implementation and algorithms to low-level designs. With HLS we designed a application specific \textit{intellectual property core} (IP-core) which we then implemented on the FPGA and perform seed selection.
dc.languageeng
dc.publisherNTNU
dc.subjectDatateknologi, Komplekse datasystemer
dc.titleOptimization of Seed Selection for Information Diffusion with High Level Synthesis
dc.typeMaster thesis
dc.source.pagenumber52


Tilhørende fil(er)

Thumbnail
Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel