TY - GEN
T1 - Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
AU - Krestinskaya, O.
AU - James, A. P.
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23
PY - 2019/1/24
Y1 - 2019/1/24
N2 - The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%.
AB - The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%.
UR - https://ieeexplore.ieee.org/document/8626224/
UR - http://www.scopus.com/inward/record.url?scp=85062302345&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626224
DO - 10.1109/NANO.2018.8626224
M3 - Conference contribution
SN - 9781538653364
BT - Proceedings of the IEEE Conference on Nanotechnology
PB - IEEE Computer [email protected]
ER -