Abstract

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.

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