16 citations · 16 across the 2 of their papers we have counts for
2 papers
cs.DC2025
A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf +6
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth i…
cs.LG2019★ 16 cited
FQ-Conv: Fully Quantized Convolution for Efficient and Accurate Inference
Bram-Ernst Verhoef, Nathan Laubeuf, Stefan Cosemans +4
Deep neural networks (DNNs) can be made hardware-efficient by reducing the numerical precision of the weights and activations of the network and by improving the network's resilien…