Memristive Neural Networks Application In Predicting of Health Disorders
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Engineering and Computer Science
Volume
2245
First Page
94
Last Page
99
Abstract
This project focuses on designing a Memristive neural network (MNN) which proves to offer the same level of accuracy when compared with CMOS based DNN but consumes low power. The designed MNNs are simulated using modern CAD simulation frameworks for 22nm technology node and compared for the total leakage power with existing CMOS based neural network systems. The simulated MNNs estimate a leakage power of ~131 μW which is less compared to CMOS based neural network’s power and an area of ~10mm2 which is comparable with the size constraints for CMOS based implementations as studied.
Keywords
CMOS based implantation, Memristor, Neural networks
Department
Electrical Engineering
Recommended Citation
Isha Baokar and Lili He. "Memristive Neural Networks Application In Predicting of Health Disorders" Lecture Notes in Engineering and Computer Science (2023): 94-99.