In this paper, a method for offline training of inverter-based memristive neural networks (IM-NNs), called ERIM, is presented. In this method, the output voltage of the inverter is modeled very accurately by considering the loading effect of the memristive crossbar. To properly choose the size of each inverter, its output load and the required slope of its voltage transfer characteristic (VTC) for an acceptable level of resiliency to the circuit element non-idealities are taken into account. The efficacy of ERIM is investigated by comparing its accuracy to those of two recently proposed offline training methods for IM-NNs (RIM and PHAX). The study is performed using IRIS, BCW, MNIST, and Fashion MNIST datasets. Simulation results show that 72% (56%) reduction in average energy consumption of the trained networks is achieved compared to RIM (PHAX) thanks to proper sizing of the inverters. In addition, due to the higher accuracy of the NN mathematical model, ERIM results in significant improvements in the match between the results of high-level modeling and HSPICE simulations while exhibiting lower sensitivity to circuit element variations.

中文翻译：

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超低功耗忆阻神经网络的负载感知可靠性改进


本文提出了一种基于逆变器的忆阻神经网络（IM-NN）的离线训练方法，称为 ERIM。在该方法中，通过考虑忆阻交叉开关的负载效应，对逆变器的输出电压进行非常准确的建模。为了正确选择每个逆变器的尺寸，需要考虑其输出负载及其电压传输特性 (VTC) 所需的斜率，以实现电路元件非理想的可接受的弹性水平。通过将 ERIM 的准确性与最近提出的两种 IM-NN 离线训练方法（RIM 和 PHAX）的准确性进行比较，研究了 ERIM 的功效。该研究使用 IRIS、BCW、MNIST 和 Fashion MNIST 数据集进行。仿真结果表明，由于逆变器的尺寸适当，与 RIM (PHAX) 相比，经过训练的网络的平均能耗降低了 72% (56%)。此外，由于神经网络数学模型的精度更高，ERIM 显着提高了高级建模和 HSPICE 仿真结果之间的匹配度，同时对电路元件变化表现出较低的敏感性。