Mr. YueHui Bao Profile

YueHui Bao

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Proceedings Article | 19 October 2023 Paper

Point distance mask attention network for learning on point cloud

YueHui Bao, Yu Gong, ShuKai Duan

Proceedings Volume 12709, 127091W (2023) https://doi.org/10.1117/12.2685007

KEYWORDS: Point clouds, Network architectures, Data modeling, Feature extraction, 3D modeling, Solid modeling, Performance modeling

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Point cloud data has been widely used in many fields, and processing them using deep learning methods has become a popular research topic. However, the irregularity and unordered data structure of point cloud makes it necessary to design neural networks that are different from those used in image and natural language processing to accommodate its characteristics. In this paper, we propose a novel network called the Point Distance Mask Attention Network. This network utilizes the position relationship between the centroid and the generated neighborhood to weight the features of each point, making the network more focused on the features close to the centroid. Additionally, we use a masking operation to ensure the permutation invariance of the network when using the ball query method to find the neighborhood. Furthermore, we propose a residual-like connection in the network architecture, which achieves better results without changing the network feature extractor and depth. We evaluated the network model on the ModelNet40 dataset and achieved an accuracy of 93.7%. Our experiments show that our network achieves better results than the original baseline network by 1.1%, with almost the same parameters and inference time.

Proceedings Article | 29 March 2023 Paper

Adaptive neuron pruning based on hybrid coding of temporal and rate

Yu Gong, YueHui Bao, ShuKai Duan

Proceedings Volume 12594, 125942N (2023) https://doi.org/10.1117/12.2671557

KEYWORDS: Neurons, Education and training, Artificial neural networks, Energy efficiency, Digital imaging, Computer hardware, Neural networks, Image processing

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Spiking neural networks have the nature of high efficiency, energy saving, and bio-interpretability. They communicate through sparse and asynchronous spikes, so they have received extensive attention in the field of neuromorphic engineering and brain-like computing. At present, the commonly used encoding methods are mainly single-rate encoding and temporal encoding. However, rate encoding cannot make use of the time information in the spike train, which has high energy consumption. Temporal encoding limits the computing power of neurons and will produce dead neurons. Moreover, it is critical to find effective solutions that reduce network complexity and improve energy efficiency while maintaining high accuracy. Therefore, we propose a hybrid coding method based on rate coding and temporal coding to solve the limitation of single coding. We propose an adaptive online pruning strategy based on hybrid coding. In this pruning strategy, 100 neurons are pruned out of the 200-neuron network, which reduces the network size and obtains a more compact network structure. The memory capacity is reduced by 1.9×, the energy efficiency is increased by 2.4×, and the classification accuracy is reduced by less than 0.5%.

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