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- W4286365561 abstract "The gate-controlled memristor has evident advantages over the traditional 2-terminal memristor in the field of the application of quantified neural networks. Since it is difficult to realize the large-scale integration, developing a flexible emulator of gate-controlled memristor is essential to explore different possible applications of neural networks. However, existing previous works of the gate-controlled memristor model lack flexibility and portability. This brief presents a novel FPGA model of gate-controlled memristor, verified on a Xilinx XQ7Z020 chip with less than 1% hardware resource utilization. And a novel approach utilizing the local linearity property of the device conductance to construct a multiply-accumulate circuit architecture has been proposed herein. Moreover, a benchmark of 4-state quantified single-layer perceptron using the proposed model has been validated with the MNIST dataset. The experimental results show that the circuit reaches the recognition accuracy of 87.6% on 10,000 images of MNIST, which differs from the PyTorch result only by 0.03%." @default.
- W4286365561 created "2022-07-21" @default.
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- W4286365561 date "2022-11-01" @default.
- W4286365561 modified "2023-10-16" @default.
- W4286365561 title "Gate-Controlled Memristor FPGA Model for Quantified Neural Network" @default.
- W4286365561 doi "https://doi.org/10.1109/tcsii.2022.3192616" @default.
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