91 citations · 377 across the 12 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.LG2019★ 1 cited
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Yue Wang, Ziyu Jiang, Xiaohan Chen +4
Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained pla…
cs.LG2019★ 48 cited
Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
Haoran You, Chaojian Li, Pengfei Xu +6
(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comp…
cs.LG2019★ 90 cited
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu +5
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widel…