Deep neural networks (DNNs) have great potential to solve many real-worl...
During the deployment of deep neural networks (DNNs) on edge devices, ma...
As data become increasingly vital for deep learning, a company would be ...
The conventional lottery ticket hypothesis (LTH) claims that there exist...
Weight pruning in deep neural networks (DNNs) can reduce storage and
com...
A primary source of increased read time on NAND flash comes from the fac...
Deep neural networks (DNNs) have shown to provide superb performance in ...
Deep neural networks (DNNs) have been proven to be effective in solving ...
There have been long-standing controversies and inconsistencies over the...
Neural network models are widely used in solving many challenging proble...
To address the large model size and intensive computation requirement of...
Deep neural networks have achieved remarkable success in computer vision...
In cloud and edge computing models, it is important that compute devices...
We study two aspects of noisy computations during inference. The first a...
We explore the robustness of recurrent neural networks when the computat...