Abstract
Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.
| 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
Approximate Probabilistic Neural Networks with Gated Threshold Logic. / Krestinskaya, O.; James Pappachen, Alex.
18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society, 2019. 8626302 (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Approximate Probabilistic Neural Networks with Gated Threshold Logic
AU - Krestinskaya, O.
AU - James Pappachen, Alex
PY - 2019/1/24
Y1 - 2019/1/24
N2 - Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.
AB - Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.
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UR - http://www.scopus.com/inward/citedby.url?scp=85062263966&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626302
DO - 10.1109/NANO.2018.8626302
M3 - Conference contribution
AN - SCOPUS:85062263966
T3 - Proceedings of the IEEE Conference on Nanotechnology
BT - 18th International Conference on Nanotechnology, NANO 2018
PB - IEEE Computer Society
ER -