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Found 3 papers, 3 papers with code
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging.
FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks
FlexiBO weights the improvement of the hypervolume of the Pareto region by the measurement cost of each objective to balance the expense of collecting new information with the knowledge gained through objective evaluations, preventing us from performing expensive measurements for little to no gain.
Transfer Learning for Performance Modeling of Deep Neural Network Systems
Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time and energy consumption.
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