Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1R) counterparts in terms of the integration density. However, current transfers of neural network weights into the conductance state of the memory devices in the crossbar architecture are accompanied by significant losses in precision due to hardware variabilities such as sneak path currents, biasing scheme effects and conductance tuning imprecision. In this work, training approaches that adapt techniques such as dropout, the reparametrization trick and regularization to TiO2 crossbar variabilities are proposed in order to generate models that are better adapted to their hardware transfers. The viability of this approach is demonstrated by comparing the outputs and precision of the proposed hardware-aware network with those of a regular fully connected network over a few thousand weight transfers using the half moons dataset in a simulation based on experimental data. For the neural network trained using the proposed hardware-aware method, 79.5% of the test set's data points can be classified with an accuracy of 95% or higher, while only 18.5% of the test set's data points can be classified with this accuracy by the regularly trained neural network.

翻译：被动式电阻随机存取存储器（ReRAM）交叉阵列作为用于模拟矩阵向量乘法的新兴技术，在集成密度方面远超其有源对应结构（1T1R）。然而，由于硬件可变性（如潜行路径电流、偏置方案效应及电导调谐不精确性），当前将神经网络权重迁移至交叉阵列架构中存储器件电导状态的过程伴随着显著的精度损失。本文提出适应TiO2交叉阵列可变性的训练方法（整合dropout、重参数化技巧及正则化等技术），以生成更适配硬件迁移的模型。通过基于实验数据的半月亮数据集仿真，将所提硬件感知网络的输出与精度同常规全连接网络进行数千次权重迁移对比，验证了该方法的可行性。经硬件感知方法训练的神经网络中，测试集79.5%的数据点可在95%及以上精度下分类，而常规训练网络仅能对18.5%的测试数据点达到该分类精度。