Memristor crossbar arrays are used in a wide range of in-memory and neuromorphic computing applications. However, memristor devices suffer from non-idealities that result in the variability of conductive states, making programming them to a desired analog conductance value extremely difficult as the device ages. In theory, memristors can be a nonlinear programmable analog resistor with memory properties that can take infinite resistive states. In practice, such memristors are hard to make, and in a crossbar, it is confined to a limited set of stable conductance values. The number of conductance levels available for a node in the crossbar is defined as the crossbar's resolution. This paper presents a technique to improve the resolution by building a super-resolution memristor crossbar with nodes having multiple memristors to generate r-simplicial sequence of unique conductance values. The wider the range and number of conductance values, the higher the crossbar's resolution. This is particularly useful in building analog neural network (ANN) layers, which are proven to be one of the go-to approaches for forming a neural network layer in implementing neuromorphic computations.

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具有超分辨率忆阻器交叉开关的模拟神经计算

忆阻器交叉开关阵列广泛用于内存中和神经形态计算应用。但是，忆阻器器件存在非理想性，导致导电状态发生变化，因此随着器件的老化，将其编程为所需的模拟电导率值非常困难。从理论上讲，忆阻器可以是非线性可编程模拟电阻器，其存储属性可以采用无限大的电阻状态。实际上，这种忆阻器很难制造，并且在交叉开关中，它仅限于有限的一组稳定电导值。纵横制中可用于节点的电导级别的数量定义为纵横制的分辨率。本文提出了一种通过建立超分辨率忆阻器交叉开关来提高分辨率的技术，该超分辨忆阻器交叉开关的节点具有多个忆阻器，以生成具有唯一电导值的r-单纯形序列。电导值的范围和数量越宽，交叉开关的分辨率越高。这在构建模拟神经网络（ANN）层时特别有用，事实证明，这是在实现神经形态计算中形成神经网络层的首选方法之一。