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 | |
| 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 |
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ASJC Scopus subject areas
- Bioengineering
- Electrical and Electronic Engineering
- Materials Chemistry
- Condensed Matter Physics
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
}
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
ER -