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.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Symposium on Circuits and Systems (ISCAS)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|State||Published - May 4 2018|