196 citations · 225 across the 4 of their papers we have counts for
4 papers
Deep Learning Technique for Human Parsing: A Survey and Outlook
Lu Yang, Wenhe Jia, Shan Li +1
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the compute…
Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
Clemens JS Schaefer, Siddharth Joshi, Shan Li +1
The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-…
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Claudionor N. Coelho, Aki Kuusela, Shan Li +7
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore r…
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
Jiong Gong, Haihao Shen, Guoming Zhang +6
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications. This paper presents the efficient inference techniq…