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The objective of this study is to illustrate the process of training a DeepNeural Network (DNN) within a Resistive RAM (ReRAM) Crossbar-based simulationenvironment using CrossSim, an Application Programming Interface (API)developed for this purpose. The CrossSim API is designed to simulate neuralnetworks while taking into account factors that may affect the accuracy ofsolutions during training on non-linear and noisy ReRAM devices. ReRAM-basedneural cores that serve as memory accelerators for digital cores on a chip cansignificantly reduce energy consumption by minimizing data transfers betweenthe processor and SRAM and DRAM. CrossSim employs lookup tables obtained fromexperimentally derived datasets of real fabricated ReRAM devices to digitallyreproduce noisy weight updates to the neural network. The CrossSim directorycomprises eight device configurations that operate at different temperaturesand are made of various materials. This study aims to analyse the results oftraining a Neural Network on the Breast Cancer Wisconsin (Diagnostic) datasetusing CrossSim, plotting the innercore weight updates and average training andvalidation loss to investigate the outcomes of all the devices.

A Deep Neural Network Deployment Based on Resistive Memory Accelerator Simulation