6 citations · 12 across the 5 of their papers we have counts for
5 papers
GreenScale: Carbon-Aware Systems for Edge Computing
Young Geun Kim, Udit Gupta, Andrew McCrabb +4
To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-a…
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning
Young Geun Kim, Carole-Jean Wu
Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collabora…
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Young Geun Kim, Carole-Jean Wu
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training sample…
Chasing Carbon: The Elusive Environmental Footprint of Computing
Udit Gupta, Young Geun Kim, Sylvia Lee +5
Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its…
AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance
Young Geun Kim, Carole-Jean Wu
Deep learning inference is increasingly run at the edge. As the programming and system stack support becomes mature, it enables acceleration opportunities within a mobile system, w…