40 citations · 46 across the 5 of their papers we have counts for
6 papers
Pinpointing the Memory Behaviors of DNN Training
Jiansong Li, Xiao Dong, Guangli Li +9
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators. Characterizing the memory behaviors of DNN train…
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices
Guangli Li, Xiu Ma, Xueying Wang +3
The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices. While a number of neural…
Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs
Xueying Wang, Guangli Li, Xiao Dong +3
Accelerating the deep learning inference is very important for real-time applications. In this paper, we propose a novel method to fuse the layers of convolutional neural networks…
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units
Guangli Li, Lei Liu, Xueying Wang +2
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision qu…
Background subtraction on depth videos with convolutional neural networks
Xueying Wang, Lei Liu, Guangli Li +3
Background subtraction is a significant component of computer vision systems. It is widely used in video surveillance, object tracking, anomaly detection, etc. A new data source fo…
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
Guangli Li, Lei Liu, Xueying Wang +3
Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent applications. The inference for the DNNs is usually performed in the cloud. However, it leads t…