35 citations · 50 across the 7 of their papers we have counts for
7 papers
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Mingze Wei, Yaomin Huang, Zhiyuan Xu +7
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly red…
CP: Channel Pruning Plug-in for Point-based Networks
Yaomin Huang, Ning Liu, Zhengping Che +7
Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success ha…
An Automatic and Efficient BERT Pruning for Edge AI Systems
Shaoyi Huang, Ning Liu, Yueying Liang +5
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained de…
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Geng Yuan, Zhiheng Liao, Xiaolong Ma +11
Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vecto…
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
Hongjia Li, Sheng Lin, Ning Liu +2
Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accur…
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates
Ning Liu, Xiaolong Ma, Zhiyuan Xu +3
Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperpar…