51 citations · 98 across the 19 of their papers we have counts for
4 papers · 1 filter
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
Nathaniel Hudson, Valerie Hayot-Sasson, Yadu Babuji +5
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks…
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
J. Gregory Pauloski, Qi Huang, Lei Huang +4
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K…
Convolutional Neural Network Training with Distributed K-FAC
J. Gregory Pauloski, Zhao Zhang, Lei Huang +2
Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kronecker-factored…