  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.
