Matches in UGent Biblio for { <https://biblio.ugent.be/publication/1977544#aggregation> ?p ?o. }
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- aggregation classification "C1".
- aggregation creator B109931.
- aggregation creator B109932.
- aggregation creator B109933.
- aggregation creator B109934.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2011".
- aggregation format "application/pdf".
- aggregation hasFormat 1977544.bibtex.
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- aggregation isPartOf urn:isbn:9781457712128.
- aggregation isPartOf urn:isbn:9781457712135.
- aggregation language "eng".
- aggregation publisher "IEEE Computer Society".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "How sensitive is processor customization to the workload's input datasets?".
- aggregation abstract "Hardware customization is an effective approach for meeting application performance requirements while achieving high levels of energy efficiency. Application-specific processors achieve high performance at low energy by tailoring their designs towards a specific workload, i.e., an application or application domain of interest. A fundamental question that has remained unanswered so far though is to what extent processor customization is sensitive to the training workload's input datasets. Current practice is to consider a single or only a few input datasets per workload during the processor design cycle - the reason being that simulation is prohibitively time-consuming which excludes considering a large number of datasets. This paper addresses this fundamental question, for the first time. In order to perform the large number of runs required to address this question in a reasonable amount of time, we first propose a mechanistic analytical model, built from first principles, that is accurate within 3.6% on average across a broad design space. The analytical model is at least 4 orders of magnitude faster than detailed cycle-accurate simulation for design space exploration. Using the model, we are able to study the sensitivity of a workload's input dataset on the optimum customized processor architecture. Considering MiBench benchmarks and 1000 datasets per benchmark, we conclude that processor customization is largely dataset-insensitive. This has an important implication in practice: a single or only a few datasets are sufficient for determining the optimum processor architecture when designing application-specific processors.".
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- aggregation similarTo SASP.2011.5941070.
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