Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks [Formula presented]
Li, Chenqi, Lammie, Corey, Amirsoleimani, Amirali, Rahimi Azghadi, Mostafa, and Genov, Roman (2023) Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks [Formula presented]. Software Impacts, 15 (March). 100473.
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Abstract
For epileptic seizure detection and prediction, to address the computational bottleneck of the von Neumann architecture, we develop an in-memory memristive crossbar-based accelerator simulator. The simulator software is composed of a Python-based neural network training component and a MATLAB-based memristive crossbar array component. The software provides a baseline network for developing deep learning-based signal processing tasks, as well as a platform to investigate the impact of weight mapping schemes and device and peripheral circuitry non-idealities.
Item ID: | 78382 |
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Item Type: | Article (Research - C1) |
ISSN: | 2665-9638 |
Keywords: | CNN, EEG, Memristive crossbar array, RRAM, Seizure detection, Seizure prediction |
Copyright Information: | © 2023 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Date Deposited: | 16 Oct 2023 23:25 |
FoR Codes: | 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400901 Analog electronics and interfaces @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation @ 50% |
SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences @ 50% |
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