6 citations · 9 across the 10 of their papers we have counts for
10 papers
Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation
Tse-Wei Chen, Wei Tao, Dongyue Zhao +4
Reducing computational costs is an important issue for development of embedded systems. Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are qua…
UNet--: Memory-Efficient and Feature-Enhanced Network Architecture based on U-Net with Reduced Skip-Connections
Lingxiao Yin, Wei Tao, Dongyue Zhao +4
U-Net models with encoder, decoder, and skip-connections components have demonstrated effectiveness in a variety of vision tasks. The skip-connections transmit fine-grained informa…
CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded Vision Systems and Applications
Tse-Wei Chen, Deyu Wang, Wei Tao +5
The field of view (FOV) of convolutional neural networks is highly related to the accuracy of inference. Dilated convolutions are known as an effective solution to the problems whi…
Hardware Architecture of Embedded Inference Accelerator and Analysis of Algorithms for Depthwise and Large-Kernel Convolutions
Tse-Wei Chen, Wei Tao, Deyu Wang +3
In order to handle modern convolutional neural networks (CNNs) efficiently, a hardware architecture of CNN inference accelerator is proposed to handle depthwise convolutions and re…
Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps
Tse-Wei Chen, Motoki Yoshinaga, Hongxing Gao +5
"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware plat…
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model
Junjie Liu, Dongchao Wen, Deyu Wang +4
Recent methods have significantly reduced the performance degradation of Binary Neural Networks (BNNs), but guaranteeing the effective and efficient training of BNNs is an unsolved…
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Junjie Liu, Dongchao Wen, Deyu Wang +4
Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Throug…
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision
Hongxing Gao, Wei Tao, Dongchao Wen +3
Deploying deep models on embedded devices has been a challenging problem since the great success of deep learning based networks. Fixed-point networks, which represent their data w…
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
Hongxing Gao, Wei Tao, Dongchao Wen +4
Deploying deep learning based face detectors on edge devices is a challenging task due to the limited computation resources. Even though binarizing the weights of a very tiny netwo…
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
Junjie Liu, Dongchao Wen, Hongxing Gao +4
Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their per…