Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

O. Krestinskaya; Alex James Pappachen

doi:10.1109/NANO.2018.8626224

Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

O. Krestinskaya, Alex James Pappachen

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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%.

Original language	English
Title of host publication	18th International Conference on Nanotechnology, NANO 2018
Publisher	IEEE Computer Society
ISBN (Electronic)	9781538653364
DOIs	https://doi.org/10.1109/NANO.2018.8626224
Publication status	Published - Jan 24 2019
Event	18th International Conference on Nanotechnology, NANO 2018 - Cork, Ireland Duration: Jul 23 2018 → Jul 26 2018

Publication series

Name	Proceedings of the IEEE Conference on Nanotechnology
Volume	2018-July
ISSN (Print)	1944-9399
ISSN (Electronic)	1944-9380

Conference

Conference	18th International Conference on Nanotechnology, NANO 2018
Country	Ireland
City	Cork
Period	7/23/18 → 7/26/18

ASJC Scopus subject areas

Bioengineering
Electrical and Electronic Engineering
Materials Chemistry
Condensed Matter Physics

Access to Document

10.1109/NANO.2018.8626224

Fingerprint Dive into the research topics of 'Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing'. Together they form a unique fingerprint.

Cite this

Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing. / Krestinskaya, O.; James Pappachen, Alex.

18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society, 2019. 8626224 (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Krestinskaya, O & James Pappachen, A 2019, Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing. in 18th International Conference on Nanotechnology, NANO 2018., 8626224, Proceedings of the IEEE Conference on Nanotechnology, vol. 2018-July, IEEE Computer Society, 18th International Conference on Nanotechnology, NANO 2018, Cork, Ireland, 7/23/18. https://doi.org/10.1109/NANO.2018.8626224

@inproceedings{6cfcc41140b44a109e80c1cb3747a7c9,

title = "Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing",

abstract = "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%.",

author = "O. Krestinskaya and {James Pappachen}, Alex",

year = "2019",

month = jan,

day = "24",

doi = "10.1109/NANO.2018.8626224",

language = "English",

series = "Proceedings of the IEEE Conference on Nanotechnology",

publisher = "IEEE Computer Society",

booktitle = "18th International Conference on Nanotechnology, NANO 2018",

address = "United States",

note = "18th International Conference on Nanotechnology, NANO 2018 ; Conference date: 23-07-2018 Through 26-07-2018",

}

TY - GEN

T1 - Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

AU - Krestinskaya, O.

AU - James Pappachen, Alex

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 - http://www.scopus.com/inward/record.url?scp=85062302345&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062302345&partnerID=8YFLogxK

U2 - 10.1109/NANO.2018.8626224

DO - 10.1109/NANO.2018.8626224

M3 - Conference contribution

AN - SCOPUS:85062302345

T3 - Proceedings of the IEEE Conference on Nanotechnology

BT - 18th International Conference on Nanotechnology, NANO 2018

PB - IEEE Computer Society

T2 - 18th International Conference on Nanotechnology, NANO 2018

Y2 - 23 July 2018 through 26 July 2018

ER -