E-viri
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Lerong Chen; Jiawen Li; Yiran Chen; Qiuping Deng; Jiyuan Shen; Xiaoyao Liang; Li Jiang
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, 2017-MarchConference Proceeding
RRAM crossbar consisting of memristor devices can naturally carry out the matrix-vector multiplication; it thereby has gained a great momentum as a highly energy-efficient accelerator for neuromorphic computing. The resistance variations and stuck-at faults in the memristor devices, however, dramatically degrade not only the chip yield, but also the classification accuracy of the neural-networks running on the RRAM crossbar. Existing hardware-based solutions cause enormous overhead and power consumption, while software-based solutions are less efficient in tolerating stuck-at faults and large variations. In this paper, we propose an accelerator-friendly neural-network training method, by leveraging the inherent self-healing capability of the neural-network, to prevent the large-weight synapses from being mapped to the abnormal memristors based on the fault/variation distribution in the RRAM crossbar. Experimental results show the proposed method can pull the classification accuracy (10%-45% loss in previous works) up close to ideal level with ≤ 1% loss.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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