13 citations · 14 across the 3 of their papers we have counts for
5 papers
Structured Pruning is All You Need for Pruning CNNs at Initialization
Yaohui Cai, Weizhe Hua, Hongzheng Chen +3
Pruning is a popular technique for reducing the model size and computational cost of convolutional neural networks (CNNs). However, a slow retraining or fine-tuning procedure is of…
Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices
Yanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou +2
Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of fa…
Contrastive Weight Regularization for Large Minibatch SGD
Qiwei Yuan, Weizhe Hua, Yi Zhou +1
The minibatch stochastic gradient descent method (SGD) is widely applied in deep learning due to its efficiency and scalability that enable training deep networks with a large volu…
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
Yichi Zhang, Ritchie Zhao, Weizhe Hua +3
We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and o…
Channel Gating Neural Networks
Weizhe Hua, Yuan Zhou, Christopher De Sa +2
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channe…