Learning Accuracy Analysis of Memristor-based Nonlinear Computing Module on Long Short-term Memory

To accelerate the training efficiency of neural network-based machine learning, a memristor-based nonlinear computing module is designed and analyzed. Nonlinear computing operation is widely needed in neuromorphic computing and deep learning. The proposed nonlinear computing module can potentially realize a monotonic nonlinear function by successively placing memristors in a series combing with a simple amplifier. The proposed module is evaluated and optimized through the Long Short-term Memory with the digit number recognition application. The proposed nonlinear computing module can reduce the chip area from microscale to nanoscale, and potentially enhance the computing efficiency to O(1) while guaranteeing accuracy. Furthermore, the impact of the resistance variation of memristor switching on the training accuracy is simulated and analyzed using Long Short-term Memory as a benchmark.