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Winograd Algorithm for AdderNet
Wenshuo Li, Hanting Chen, Mingqiang Huang +3
Adder neural network (AdderNet) is a new kind of deep model that replaces the original massive multiplications in convolutions by additions while preserving the high performance. S…
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Yunhe Wang, Mingqiang Huang, Kai Han +4
Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally inten…
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Yehui Tang, Yunhe Wang, Yixing Xu +4
Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters signifi…
Positive-Unlabeled Compression on the Cloud
Yixing Xu, Yunhe Wang, Hanting Chen +4
Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providin…
ReNAS:Relativistic Evaluation of Neural Architecture Search
Yixing Xu, Yunhe Wang, Kai Han +4
An effective and efficient architecture performance evaluation scheme is essential for the success of Neural Architecture Search (NAS). To save computational cost, most of existing…
Searching for Accurate Binary Neural Architectures
Mingzhu Shen, Kai Han, Chunjing Xu +1
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and featu…