The objective of this study is to illustrate the process of training a Deep Neural Network (DNN) within a Resistive RAM (ReRAM) Crossbar-based simulation environment using CrossSim, an Application Programming Interface (API) developed for this purpose. The CrossSim API is designed to simulate neural networks while taking into account factors that may affect the accuracy of solutions during training on non-linear and noisy ReRAM devices. ReRAM-based neural cores that serve as memory accelerators for digital cores on a chip can significantly reduce energy consumption by minimizing data transfers between the processor and SRAM and DRAM. CrossSim employs lookup tables obtained from experimentally derived datasets of real fabricated ReRAM devices to digitally reproduce noisy weight updates to the neural network. The CrossSim directory comprises eight device configurations that operate at different temperatures and are made of various materials. This study aims to analyse the results of training a Neural Network on the Breast Cancer Wisconsin (Diagnostic) dataset using CrossSim, plotting the innercore weight updates and average training and validation loss to investigate the outcomes of all the devices.

翻译：本研究旨在阐述在基于阻变存储器（ReRAM）交叉阵列的仿真环境中，利用为此开发的应用程序编程接口（API）CrossSim训练深度神经网络（DNN）的过程。CrossSim API旨在模拟神经网络，同时考虑在非线性且存在噪声的ReRAM器件上进行训练时可能影响求解精度的因素。作为芯片上数字核心的内存加速器，基于ReRAM的神经核心可通过最小化处理器与SRAM及DRAM之间的数据传输来显著降低能耗。CrossSim利用从实际制造的ReRAM器件实验数据集获得的查找表，以数字方式重现神经网络中带有噪声的权重更新。CrossSim目录包含八种在不同温度下运行且由不同材料构成的器件配置。本研究旨在分析利用CrossSim在威斯康星州乳腺癌（诊断）数据集上训练神经网络的结果，通过绘制内核权重更新以及平均训练和验证损失来研究所有器件的输出结果。