TY - GEN
T1 - Analog Backpropagation Learning Circuits for Memristive Crossbar Neural Networks
AU - Krestinskaya, Olga
AU - Salama, Khaled N.
AU - James, Alex Pappachen
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2018/5/4
Y1 - 2018/5/4
N2 - The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are validated comprehensively using the relevant transfer characteristics and transient response of individual circuit modules.
AB - The implementation of backpropagation algorithm using gradient descent operation with analog circuits is an open problem. In this paper, we present the analog learning circuits for realizing backpropagation algorithm for use with neural networks in memristive crossbar arrays. The circuits are simulated in SPICE using TSMC 180nm CMOS process models, and HP memristor models. The gradient descent operations are validated comprehensively using the relevant transfer characteristics and transient response of individual circuit modules.
UR - http://hdl.handle.net/10754/630386
UR - https://ieeexplore.ieee.org/document/8351344/
UR - http://www.scopus.com/inward/record.url?scp=85053923154&partnerID=8YFLogxK
U2 - 10.1109/iscas.2018.8351344
DO - 10.1109/iscas.2018.8351344
M3 - Conference contribution
SN - 9781538648810
BT - 2018 IEEE International Symposium on Circuits and Systems (ISCAS)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -