Matches in SemOpenAlex for { <https://semopenalex.org/work/W2129445095> ?p ?o ?g. }
Showing items 1 to 52 of
52
with 100 items per page.
- W2129445095 abstract "The main aim of this short paper is to propose a new branch prediction approach called by us branch prediction. We developed a first neural predictor model based on a simple neural learning algorithm, known as learning vector quantization algorithm. Based on a trace driven simulation method we investigated the influences of the learning step, training processes, etc. Also we compared the neural predictor with a powerful classical predictor and we establish that they result in close performances. Therefore, we conclude that in the near future it might be necessary to model and simulate other more powerful neural adaptive predictors, based on more efficient neural networks architectures, in order to obtain better prediction accuracies compared with the previous known schemes." @default.
- W2129445095 created "2016-06-24" @default.
- W2129445095 creator A5033864373 @default.
- W2129445095 creator A5087080522 @default.
- W2129445095 date "2003-01-22" @default.
- W2129445095 modified "2023-10-17" @default.
- W2129445095 title "Towards a high performance neural branch predictor" @default.
- W2129445095 cites W2028624242 @default.
- W2129445095 cites W2051777799 @default.
- W2129445095 cites W2119077081 @default.
- W2129445095 cites W2130363691 @default.
- W2129445095 cites W2157364313 @default.
- W2129445095 cites W2159268023 @default.
- W2129445095 cites W4299101042 @default.
- W2129445095 doi "https://doi.org/10.1109/ijcnn.1999.831066" @default.
- W2129445095 hasPublicationYear "2003" @default.
- W2129445095 type Work @default.
- W2129445095 sameAs 2129445095 @default.
- W2129445095 citedByCount "43" @default.
- W2129445095 countsByYear W21294450952014 @default.
- W2129445095 countsByYear W21294450952016 @default.
- W2129445095 countsByYear W21294450952018 @default.
- W2129445095 countsByYear W21294450952019 @default.
- W2129445095 countsByYear W21294450952020 @default.
- W2129445095 countsByYear W21294450952021 @default.
- W2129445095 countsByYear W21294450952022 @default.
- W2129445095 crossrefType "proceedings-article" @default.
- W2129445095 hasAuthorship W2129445095A5033864373 @default.
- W2129445095 hasAuthorship W2129445095A5087080522 @default.
- W2129445095 hasConcept C168522837 @default.
- W2129445095 hasConcept C173608175 @default.
- W2129445095 hasConcept C41008148 @default.
- W2129445095 hasConceptScore W2129445095C168522837 @default.
- W2129445095 hasConceptScore W2129445095C173608175 @default.
- W2129445095 hasConceptScore W2129445095C41008148 @default.
- W2129445095 hasLocation W21294450951 @default.
- W2129445095 hasOpenAccess W2129445095 @default.
- W2129445095 hasPrimaryLocation W21294450951 @default.
- W2129445095 hasRelatedWork W1498229408 @default.
- W2129445095 hasRelatedWork W1595868330 @default.
- W2129445095 hasRelatedWork W2041407159 @default.
- W2129445095 hasRelatedWork W2051612220 @default.
- W2129445095 hasRelatedWork W2086475872 @default.
- W2129445095 hasRelatedWork W2149827666 @default.
- W2129445095 hasRelatedWork W2161646044 @default.
- W2129445095 hasRelatedWork W2299668876 @default.
- W2129445095 hasRelatedWork W2774015159 @default.
- W2129445095 hasRelatedWork W5430021 @default.
- W2129445095 isParatext "false" @default.
- W2129445095 isRetracted "false" @default.
- W2129445095 magId "2129445095" @default.
- W2129445095 workType "article" @default.