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Fusion of Image Storage and Operation Based on Ag-Chalcogenide Memristor with Synaptic Plasticity

机译:基于Ag-硫族化物忆阻器和突触可塑性的图像存储和操作融合

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With the discovery of the memristor devices, most researchers have focused on the simulation and application of memristive characteristics, especially the nonvolatile storage potential of the memristor. However, the fusion of image storage and operation is seldom discussed. Therefore, this paper focuses on the electrical characterization of silver chalcogenide based on memristor devices, which can be used as a synapse to mimic the STDP and SRDP learning rules in neuromorphic systems. Then on this basis, a improved memristive cross array structure is designed, whose storage and operation are set in a single unit. Furthermore, this improved memristive cross array and image processing are combined with adjusting the memristor resistance by synaptic plasticity to achieve the integration of storage and processing. Finally, the effectiveness of the design is verified by the LTSPICE experiments of the nonlinear grayscale transformation and logic operation of the image. The design breaks the gap between the processing and storage in the Von Neumann architecture, which will be helpful to the development of the parallel computing in the future.
机译:随着忆阻器器件的发现,大多数研究人员将注意力集中在忆阻特性的仿真和应用上,尤其是忆阻器的非易失性存储潜力。但是,很少讨论图像存储和操作的融合。因此,本文着重研究基于忆阻器器件的硫族硫化银的电学表征,可作为突触模仿神经形态系统中的STDP和SRDP学习规则。然后在此基础上,设计了一种改进的忆阻交叉阵列结构,其存储和操作设置在一个单元中。此外,这种改进的忆阻性交叉阵列和图像处理与通过突触可塑性调节忆阻器电阻相结合,实现了存储和处理的整合。最后,通过对图像进行非线性灰度变换和逻辑运算的LTSPICE实验,验证了设计的有效性。该设计打破了冯·诺依曼(Von Neumann)架构中处理与存储之间的空白,这将有助于未来并行计算的发展。

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