A Simulation Framework for Memristive Deep Learning Systems
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Updated
May 13, 2024 - Python
A Simulation Framework for Memristive Deep Learning Systems
This repository includes the Resistive Random Access Memory (RRAM) Compiler which is designed in the context of the research project of Dimitris Antoniadis (PG Taught Student) at Imperial College London
NI RRAM programming in Python
Code and repository for RRAM RADAR programming method: https://doi.org/10.1109/TED.2021.3097975
Series of ReRAM characterization modules compatible with Keithley Semiconductor Characterization Systemsinstruments
A well-posed RRAM SPICE model implemented in Verilog-A, based on Stanford/ASU filamentary model, using code developed at UC Berkeley
Scripts to model functional experimental or other phenomena, such as neuronal/device spiking, or tip-sample interactions.
Memristor model: Various implementations of the simplified memristor model "JART-TUD VCM"
RRAM resistive switching behavior evaluation and prediction, based on fabrication conditions. Applied Machine learning\Deep learning models to predict SET voltage distribution in Honey-based RRAM devices. Simulating the basic operations of RRAM crossbars in image classification tasks, investigating the robustness of in situ vs. ex situ training.
Long Short-Term Memory Implementation Exploiting Passive RRAM Crossbar Array
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