An Efficient and Low-Cost FPGAs-Accelerated CNN-Based Edge Intelligent Garbage Classification System on ZYNQ

Garbage classification has achieved a high classification accuracy using convolutional neural network (CNN). However, due to the high complexity of CNNs, CPU or GPU-based solutions are too expensive for daily garbage classification. In this paper, we propose an efficient and low-cost field programmable logic gate arrays (FPGAs) accelerated CNN-based intelligent garbage classification system with two ARM+FPGA heterogeneous platforms. In this work, an image preprocessing module is deployed in the programmable logic of xc7z010clg400-1 to perform the real-time preprocessing of a garbage image. A pre-trained 18-layer CNN model is deployed in the programmable logic of xc7z020clg400-1 with the help of hardware computing module reusing to perform the garbage classification in real time. In the processing system of xc7z010clg400-1 and xc7z020clg400-1, a software to implement the scheduling of hardware is designed. According to the experiments, the system achieved a calculation speed of 21.709 times faster than the Intel(R) Core(TM) i7-7700HQ processor (CPU) and the power consumption is 1/17 of the CPU's. Its classification accuracy on the garbage dataset TrashNet is 93%, which is only 0.333% less than the pure software on the CPU. It is a cheaper solution and can also enhance the efficiency of daily garbage classification based on embedded devices. In the future work, the system we proposed will be a promising solution of the large consumption of time, energy and comnuting resource in previous solutions.