Simulation of memristive crossbar arrays for seizure detection and prediction using parallel Convolutional Neural Networks

被引：3

作者：

Li, Chenqi ^{[1
]}

Lammie, Corey ^{[2
]}

Amirsoleimani, Amirali ^{[3
]}

Azghadi, Mostafa Rahimi ^{[2
]}

Genov, Roman ^{[4
]}

机构：

[1] Univ Toronto, Div Engn Sci, 42 St George St, Toronto, ON M5S 2E4, Canada

[2] James Cook Univ, Coll Sci & Engn, 1 James Cook Dr, Townsville, Qld 4811, Australia

[3] York Univ, Dept Elect Engn & Comp Sci, 4700 Keele St, Toronto, ON M3J 1P3, Canada

[4] Univ Toronto, Dept Elect & Comp Engn, 42 St George St, Toronto, ON M5S 2E4, Canada

来源：

SOFTWARE IMPACTS | 2023年 / 15卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

CNN; Seizure detection; Seizure prediction; EEG; RRAM; Memristive crossbar array;

D O I：

10.1016/j.simpa.2023.100473

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页数：4

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