64 citations · 125 across the 38 of their papers we have counts for
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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…
cs.DC2018
A Highly Parallel FPGA Implementation of Sparse Neural Network Training
Sourya Dey, Diandian Chen, Zongyang Li +4
We demonstrate an FPGA implementation of a parallel and reconfigurable architecture for sparse neural networks, capable of on-chip training and inference. The network connectivity…