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
Java Application for automated memristor experiments and data collection based on Digilent's Analog Discovery 2 USB Oscilloscope and Knowm's Memristor Discovery Board.
A Python tool for computing and plotting currents and voltages in passive crossbar arrays.
[Nature Machine Intelligence 2023] "Echo state graph neural networks with analogue random resistive memory arrays."
Structured clustering for memristive crossbar based neuromorphic architectures
In-sensor reservoir computing for language learning via two-dimensional memristors
Code used in the paper “Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks”
Nengo implementation of neuromorphic learning using simulated SrTiO3 memristors.
HSPICE implementation of hybrid CMOS/Memristor BAM
Implementation of "Low-Cost and Effective Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks." 2021 58th ACM/IEEE Design Automation Conference (DAC).
Control algorithms and logic for Memristor's robots
LTSpice simulation software is used to study the behavior of a Memristor. Different logic gates like NOR, NAND and XOR were modelled and simulated followed by the simulation of a memristor based full-adder.
Memristor model: Various implementations of the simplified memristor model "JART-TUD VCM"
Simple model of a memristor
Two-Level Quantum Memristor implemented both numerically and through a quantum circuit using the Qiskit SDK
V-I Diagram Plotter using SerialPort in C#
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