Publication | Closed Access
Convolution engine
171
Citations
19
References
2013
Year
Unknown Venue
Cluster ComputingHeterogeneous ComputingEfficiency GainsEngineeringComputer ArchitectureData StorageCompute KernelData ScienceHigh-performance ArchitectureData IntegrationParallel ComputingData ManagementHigh-performance Data AnalyticsComputer EngineeringComputer ScienceData-intensive ComputingProgram AnalysisParallel ProgrammingSpecialized ComputingSystem Software
This paper focuses on the trade-off between flexibility and efficiency in specialized computing. We observe that specialized units achieve most of their efficiency gains by tuning data storage and compute structures and their connectivity to the data-flow and data-locality patterns in the kernels. Hence, by identifying key data-flow patterns used in a domain, we can create efficient engines that can be programmed and reused across a wide range of applications.
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