应用于智能芯片的可视化反馈系统研究

当前，市场上普遍使用的负责推理的终端人工智能（AI）芯片使用训练好的参数对数据进行快速高效运算。但在通常训练过程中使用的数据集和真实数据的分布不一致，由此获得的参数会导致终端AI芯片识别准确度降低。为此，提出了一种基于终端AI芯片的可视化反馈系统架构方法。使用反卷积特征可视化方法，在具有高效计算性能的终端AI芯片上，对卷积核参数进行迭代优化，达到可识别该图像目的。相比于CPU/GPU和FPGA，所提架构在卷积神经网络模型里，更具有高效处理能力和灵活可塑性。实验表明，该研究有效提高了终端AI芯片的普适性、识别准确度和处理效率。 

Visual feedback system applied to AI chips

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.