Analyzing the Effects of Non-Ideal Synaptic Devices on Computing-in-Memory with Online Training Using the Accumulated Weight Update Algorithm

In this study, we present CIMulator, a simulation platform for crossbar arrays based on synaptic element types such as resistive random access memory (RRAM), ferroelectric field-effect transistor (FeFET), and phase change memory (PCM) devices. We have developed a custom-made synapse model for FeFET and adopted non-linear weight update expressions for RRAM and PCM to study the non-ideal behaviors and device variations extensively. To reduce the required distinguishable conductance levels of the devices, advanced methods such as the accumulated weight update method and novel architectures such as 1D1S were employed, resulting in great results with reduced efforts. However, this methodology may not be advantageous for all synaptic devices and at all times. Therefore, we present the most viable neural network solution based on device characteristics. Our results demonstrate that the proposed simulation platform can effectively model analog synaptic devices and provide insight into their non-ideal behaviors and device variations, contributing to the development of efficient and highperformance neuromorphic computing systems.