Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms

By D. Pandiyan, Carole-Jean Wu. 2014

84 citations recorded by the source corpus.

How researchers see this paper

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Understanding the Future of Energy Efficiency in Multi-Module GPUs (2019, methodology): “Overall, GPUJoule’s estimation and validation process is fairly comparable to prior works that focus on instruction-based energy estimation in the server [27], mobile [28], and many-core architecture [26] domains but adjusted for the unique properties of GPU architectures.”
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning (2021, methodology): “Similar power measurement methodologies are used in prior works [93, 94, 109].”
AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, methodology): “We measure the system-wide power consumption of the smartphones using an external Monsoon Power Meter [80]; prior works used the similar practice [8], [11], [88].”
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, methodology): “the system-wide power consumption of the smartphones using an external Monsoon Power Meter [68] – similar practice is used in a number of prior works [6, 9, 75].”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “All these steps consume substantial time and energy in order to bring the data into the CPU chip [4, 7, 151, 152].”