TY - GEN
T1 - Approximate Probabilistic Neural Networks with Gated Threshold Logic
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 - 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.
UR - https://ieeexplore.ieee.org/document/8626302/
UR - http://www.scopus.com/inward/record.url?scp=85062263966&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626302
DO - 10.1109/NANO.2018.8626302
M3 - Conference contribution
SN - 9781538653364
BT - Proceedings of the IEEE Conference on Nanotechnology
PB - IEEE Computer [email protected]
ER -