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    Hu Fei, You Zhiqiang, Liu Peng, Kuang Jishun. Circuit Design of Convolutional Neural Network Based on Memristor Crossbar Arrays[J]. Journal of Computer Research and Development, 2018, 55(5): 1097-1107. DOI: 10.7544/issn1000-1239.2018.20170107
    Citation: Hu Fei, You Zhiqiang, Liu Peng, Kuang Jishun. Circuit Design of Convolutional Neural Network Based on Memristor Crossbar Arrays[J]. Journal of Computer Research and Development, 2018, 55(5): 1097-1107. DOI: 10.7544/issn1000-1239.2018.20170107

    Circuit Design of Convolutional Neural Network Based on Memristor Crossbar Arrays

    • Memristor crossbar array has caused wide attention due to its excellent performance in neuromorphic computing. In this paper, we design a circuit to realize a convolutional neural network (CNN) using memristors and CMOS devices. Firstly, we improve a memristor crossbar array that can store weights and bias accurately. A dot product between two vectors can be calculated after introducing an appropriate encoding scheme. The improved memristor crossbar array is employed for convolution and pooling operations, and a classifier in a CNN. Secondly, we also design a memristive CNN architecture using the improved memristor crossbar array and based on the high fault-tolerance of CNNs to perform a basic CNN algorithm. In the designed architecture, the analog results of convolution operations are sampled and held before a pooling operation rather than using analog digital converters and digital analog converters between convolution and pooling operations in a previous architecture. Experimental results show the designed circuit with the area of 0.8525cm\+2 can achieve a speedup of 1770×compared with a GPU platform. Compared with previous memristor-based architecture with a similar area, our design is 7.7×faster. The average recognition errors performed on the designed circuit are only 0.039% and 0.012% lost than those of software implementation in the cases of a memristor with 6-bit and 8-bit storage capacities, respectively.
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