2021 · 18 citations · 11 references
ComputingComputer EngineeringHigh-performance ComputingEdge ComputingHeterogeneous ComputingPerformance MeasurementEdge ArchitectureEdge Computing InfrastructureEdge DevicesEdge InfrastructurePerformance ModelingPower ManagementEdge Computing PerformanceEdge Energy EfficiencyEdge ScalabilityEnergy-efficient Edge ComputingPower ConsumptionPower Consumption ModelingRuntime PerformanceVariability Analysis
While applications deployed at the edge often rely on performance stability (or, at a minimum, on a predictable level of performance), variability at the edge remains a real problem [4]. This study uncovers a surprising source of variability: intrinsic variability (in performance and power consumption) among edge platforms that are nominally identical. We focus on a popular platform designed for edge applications, the NVIDIA Jetson AGX, and aim to answer the following high-level questions through rigorous statistical analysis: (i) are the edge devices in our study statistically different from each other in terms of applications' runtime performance and power draw (although they are sold under the same product model and family)?, (ii) if the differences between these edge devices are statistically significant, what is the magnitude of these differences?, and (iii) do these differences matter from the application's perspective?
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Machine Learning at Facebook: Understanding Inference at the Edge
Carole-Jean Wu, David Brooks, Kevin Chen et al. · 2019 · 448 citations
Torchvision the machine-vision package of torch
Sébastien Marcel, Yann Rodriguez · 2010 · 422 citations