2 citations · 4 across the 3 of their papers we have counts for
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cs.LG2024★ 2 cited
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Dingyi Dai, Yichi Zhang, Jiahao Zhang +4
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix mult…
cs.LG2021
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Qi Sun, Hexin Dong, Zewei Chen +3
Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradie…
cs.LG2020★ 2 cited
A Practical Layer-Parallel Training Algorithm for Residual Networks
Qi Sun, Hexin Dong, Zewei Chen +5
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which a…