Efficient Training Method for Memristor-Based Array Using 1T1M Synapse

In this brief, an efficient training method for memristor-based array (crossbar) with one transistor and one memristor (1T1M) synapse is proposed, which enables parallel update of memristor-based arrays trained by stochastic gradient descent within two steps. Voltage ThrEshold Adaptive Memristor (VTEAM) model is utilized to describe memristor characteristics for simulations. On this basis, circuit parameters optimization method compensating the asymmetric and nonlinear weight update is provided for better training results. The effectiveness of proposed training method is evaluated on OR, AND functions and digit recognition task. Simulation results demonstrate the robustness of proposed training method to electrical noise and imperfections of memristors.