12 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.DC2022
QuantPipe: Applying Adaptive Post-Training Quantization for Distributed Transformer Pipelines in Dynamic Edge Environments
Haonan Wang, Connor Imes, Souvik Kundu +3
Pipeline parallelism has achieved great success in deploying large-scale transformer models in cloud environments, but has received less attention in edge environments. Unlike in c…
cs.DC2021★ 12 cited
Pipeline Parallelism for Inference on Heterogeneous Edge Computing
Yang Hu, Connor Imes, Xuanang Zhao +4
Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP). However, these large-scale mod…
eess.IV2020
RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation
Andrew Rittenbach, John Paul Walters
Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes a…