11 citations · 17 across the 3 of their papers we have counts for
5 papers
Structural Pruning in Deep Neural Networks: A Small-World Approach
Gokul Krishnan, Xiaocong Du, Yu Cao
Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary…
CGaP: Continuous Growth and Pruning for Efficient Deep Learning
Xiaocong Du, Zheng Li, Yu Cao
Today a canonical approach to reduce the computation cost of Deep Neural Networks (DNNs) is to pre-define an over-parameterized model before training to guarantee the learning capa…
Towards Efficient Neural Networks On-a-chip: Joint Hardware-Algorithm Approaches
Xiaocong Du, Gokul Krishnan, Abinash Mohanty +3
Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challe…
Single-Net Continual Learning with Progressive Segmented Training (PST)
Xiaocong Du, Gouranga Charan, Frank Liu +1
There is an increasing need of continual learning in dynamic systems, such as the self-driving vehicle, the surveillance drone, and the robotic system. Such a system requires learn…
Efficient Network Construction through Structural Plasticity
Xiaocong Du, Zheng Li, Yufei Ma +1
Deep Neural Networks (DNNs) on hardware is facing excessive computation cost due to the massive number of parameters. A typical training pipeline to mitigate over-parameterization…