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首页> 外文期刊>Journal of Physics, D. Applied Physics: A Europhysics Journal >Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications
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Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

机译：模型框架及忆阻窗口对神经形态应用的相关框架和比较

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This paper presents a comparative synthesis of the suitability of three memristive device technologies and their corresponding spike-timing-dependent plasticity (STDP) learning windows for neuromorphic applications. The physical mechanisms behind the nonlinear switching memristive dynamics of ReRAM, based on titanium dioxide, ferroelectric tunnel junctions, and phase change memory are analyzed towards the development of accurate and computationally efficient compact models which are implemented as a Verilog-A description. The developed Verilog-A compact models are separately validated and compared with the measurement data. Moreover, the asynchronous STDP learning rule is implemented using the above mentioned memristive devices as artificial synapse for spike-based neuromorphic computing. The considered memristive technologies are compared and discussed towards their integration in fast and/or large-scale circuit implementations.
机译：本文介绍了三个椎间型器件技术及其对应的峰值定时依赖性塑性（STDP）学习窗的比较合成，用于神经形态应用。 基于二氧化钛，铁电隧道结和相变存储器的RERAM的非线性切换忆阻动力学的物理机制分析了精确和计算有效的紧凑型模型，实现为Verilog-A描述。 开发的Verilog-A紧凑的模型是单独验证的，并与测量数据进行比较。 此外，使用上述忆耳器件作为峰值的神经形态计算的人工突触来实现异步STDP学习规则。 比较了考虑的铭文技术，并朝着它们在快速和/或大规模电路实现中讨论了它们的集成。

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