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Time-Dependent Variability in RRAM-based Analog Neuromorphic System for Pattern Recognition

机译:基于RRAM的模拟神经形态系统中模式识别的时变性

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摘要

For the first time, this work investigated the time dependent variability (TDV) in RRAMs and its interaction with the RRAM-based analog neuromorphic circuits for pattern recognition. It is found that even the circuits are well trained, the TDV effect can introduce non-negligible recognition accuracy drop during the operating condition. The impact of TDV on the neuromorphic circuits increases when higher resistances are used for the circuit implementation, challenging for the future low power operation. In addition, the impact of TDV cannot be suppressed by either scaling up with more synapses or increasing the response time and thus threatens both real-time and general-purpose applications with high accuracy requirements. Further study on different circuit configurations, operating conditions and training algorithms, provides guidelines for the practical hardware implementation.
机译:这项工作首次研究了RRAM中的时间相关变异性(TDV)及其与基于RRAM的模拟神经形态电路的交互作用,以进行模式识别。已经发现,即使电路训练有素,TDV效应也会在工作条件下引起不可忽略的识别精度下降。当将更高的电阻用于电路实现时,TDV对神经形态电路的影响会增加,这对未来的低功耗操作提出了挑战。另外,不能通过增加更多的突触或增加响应时间来抑制TDV的影响,从而威胁对具有高精度要求的实时和通用应用程序的威胁。对不同电路配置,工作条件和训练算法的进一步研究,为实际的硬件实现提供了指导。

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