| Citation: | LI Xinzhi, DONG Shengbo, CUI Xiangyang, et al. Visual feedback system applied to AI chips[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1494-1502. doi: 10.13700/j.bh.1001-5965.2019.0518(in Chinese)
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Currently, terminal Artificial Intelligence (AI) chips responsible for inference are commonly used in the market, which use trained parameters to perform fast and efficient calculations on data. However, the training dataset usually has different distribution with the real-world data, and the parameters obtained in this case lead to a decrease in the accuracy of the terminal chip recognition. To this end, this paper proposes a visual feedback system architecture method based on terminal AI chip. Using the deconvolution feature visualization method, the convolution kernel parameters are iteratively optimized on the terminal AI chip with high computational performance to achieve the purpose of recognizing the image. Compared with CPU/GPU and FPGA, the architecture proposed in this paper has more efficient processing capability and flexible plasticity in the convolutional neural network model. Experiments show that the research effectively improves the universality, recognition accuracy and handling efficiency of the terminal chip.
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