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- W4210363906 abstract "Memristor crossbar array (MCA) based accelerators can be used to accelerate neural networks. However, the conductances of memristors change in the inference processes because of drift phenomena. As the weights are represented as the conductances, the weights also change slowly and the accuracy of neural network degrades. In this paper, we propose two techniques to slow down the accuracy degradation by recovering the accuracy after drifting. Firstly, we introduce one label memristor to monitor the drift degree of the crossbar and change the current-result conversion parameter to recover the accuracy. Secondly, we propose an auto-correction technique to correct the conductance of the label memristor. The conductance of the label memristor is used to change the parameter which is used in current-result conversion phase. Experimental results show that our proposed techniques can increase up to 8× high accuracy (> 95%) time and 3× lifetime (> 80%) for MCA based neural networks." @default.
- W4210363906 created "2022-02-08" @default.
- W4210363906 creator A5077452145 @default.
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- W4210363906 date "2021-11-22" @default.
- W4210363906 modified "2023-09-29" @default.
- W4210363906 title "Efficient Techniques for Extending Service Time for Memristor-based Neural Networks" @default.
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- W4210363906 doi "https://doi.org/10.1109/apccas51387.2021.9687674" @default.
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