The superiority of convolutional neural network (CNN) has been proven in various object recognition tasks and has received much attention. However, the modern CNN approaches usually that assume the testing data and the training data belong to an identical category. Therefore, the current CNN approaches are not efficient for some applications with multisource data (i.e., the input data come from different sources), such as remote sensing scene. To increase the adaptability of the involved CNN approach, we first propose a $K$ -means-assisted scenario-aware reconfigurable convolutional neural network (KASR - CNN) mechanism. The KASR-CNN is composed of a fully convolutional autoencoder-based $K$ -means clustering (FCA-KC) and a reconfigurable convolutional neural network (RCNN), which are used to perform the coarse-grained classification and fine-grained classification to the input data, respectively. Furthermore, a Lego-like architecture design methodology is proposed to reduce the design complexity and improve computing flexibility. To show the adaptability, the KASR-CNN mechanism has been applied to different CNN models. In addition, the KASR-CNN has been verified on the Xilinx Zynq-ZC706 field-programmable gate array (FPGA) and implemented with TSMC 40-nm technology. Compared with the conventional approaches, the proposed KASR-CNN can help the involved CNN model to improve 3.73%–36.65% classification accuracy with only 0.38%–0.74% area overhead.

中文翻译：

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等级制度 ķ均值辅助的场景感知可重构卷积神经网络

卷积神经网络（CNN）的优越性已在各种对象识别任务中得到证明，并受到了广泛关注。但是，现代的CNN方法通常假定测试数据和训练数据属于同一类别。因此，当前的CNN方法对于具有多源数据（即，输入数据来自不同源）的某些应用（例如遥感场景）而言效率不高。为了提高所涉及的CNN方法的适应性，我们首先提出 $ K $ 均值辅助场景感知的可重构卷积神经网络（KASR -- CNN）机制。KASR-CNN由基于全卷积自动编码器的 $ K $ -均值聚类（FCA-KC）和可重构卷积神经网络（RCNN），它们分别用于对输入数据进行粗粒度分类和细粒度分类。此外，提出了一种类似乐高的架构设计方法，以降低设计复杂度并提高计算灵活性。为了显示适应性，KASR-CNN机制已应用于不同的CNN模型。此外，KASR-CNN已在Xilinx Zynq-ZC706现场可编程门阵列（FPGA）上进行了验证，并已通过TSMC 40-nm技术实现。与传统方法相比，提出的KASR-CNN可以帮助所涉及的CNN模型在仅占0.38％-0.74％的区域开销的情况下提高3.73％-36.65％的分类精度。