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Found 3 papers, 1 papers with code
Hardware-Aware Machine Learning: Modeling and Optimization
What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)?
HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks
While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challenges to designers and Machine Learning (ML) practitioners, especially when power and memory constraints need to be considered.
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks
We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy.
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