Dequan Wang 王德泉
Dequan Wang 王德泉
Home
Publications
CV
Light
Dark
Automatic
system
GACT: Activation Compressed Training for Generic Network Architectures
GACT is an activation compression training (ACT) framework to support a broad range of machine learning tasks for generic neural network architectures with limited domain knowledge. By analyzing a linearized version of ACT’s approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture.
Xiaoxuan Liu
,
Lianmin Zheng
,
Dequan Wang
,
Yukuo Cen
,
Weize Chen
,
Xu Han
,
Jianfei Chen
,
Zhiyuan Liu
,
Jie Tang
,
Joseph Gonzalez
,
Michael Mahoney
,
Alvin Cheung
PDF
Cite
Code
arXiv
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
ActNN is a PyTorch library for memory-efficient training. It reduces the training memory footprint by compressing the saved activations. ActNN is implemented as a collection of memory-saving layers. These layers have an identical interface to their PyTorch counterparts.
Jianfei Chen
,
Lianmin Zheng
,
Zhewei Yao
,
Dequan Wang
,
Ion Stoica
,
Michael Mahoney
,
Joseph Gonzalez
PDF
Cite
Code
机器之心
arXiv
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Co-design of a deformable convolution operation on FPGA with hardware-friendly modifications, showing up to 9.76× hardware speedup. Development of an efficient DNN model for object detection with co-designed input-adaptive deformable convolution that achieves 67.1 AP50 on Pascal VOC with 2.9 MB parameters. The model is 20.9× smaller but 10% more accurate than the Tiny-YOLO. Implementation of an FPGA accelerator to support the target neural network design that runs at 26 frames per second on Pascal VOC with 61.7 AP50.
Qijing Huang
,
Dequan Wang
,
Zheng Dong
,
Yizhao Gao
,
Yaohui Cai
,
Bichen Wu
,
Kurt Keutzer
,
John Wawrzynek
PDF
Cite
Code
arXiv
Cite
×