记忆电阻交叉栅用于神经形态计算的可能性和局限性

O. Telminov, Eugeny Gornev
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引用次数: 0

摘要

基于新物理原理(ReRAM, FRAM等)的新型神经形态元件基础-非易失性存储元件的广泛开发。这些内存元件用于实现交叉杆结构中可编程的突触权重,并使神经网络能够进行内存计算。然而,潜流和漏流严重限制了横杆行和柱的可实现尺寸。考虑了神经网络在忆阻器横栅上实现的特点。
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Possibilities and Limitations of Memristor Crossbars for Neuromorphic Computing
Extensive development of new neuromorphic element base – non-volatile memory elements based on new physical principles (ReRAM, FRAM etc.) is conducted. These memory elements are used to implement programmable synaptic weights in crossbar architecture, and enable neural network to conduct in-memory computations. However, the sneak currents and leakage currents are a serious limitation on the achievable dimensionality of rows and columns of the crossbar. The features of the implementation of neural networks on memristor crossbars are considered.
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