GST-memristor-based online learning neural networks

Abstract At present, it is an urgent issue to effectively train artificial neural network (ANN), especially when the data is large. Online learning has been used to solve the problem, most of which is based on least mean square (LMS). However, it is inefficient to implement the LMS on conventional digital hardware, because of the physical separation between the memory arrays and arithmetic module. To solve this problem, CMOS has been utilized. However, it costs too many powers and areas while designing CMOS synapses in the very large scale integrated (VLSI) circuit. As a novel device, memristor is believed to overcome this shortcoming as memristors could be utilized to store the weights which could be changed by a voltage pulse. The filamentary bipolar memristive switching in Ge 2 Sb 2 Te 5 (GST) has been proved to be an ideal choice for memristive materials. And it has two states—amorphous and crystalline, which can be changed by DC sweep. In this paper, we consider an artificial synapse which includes a GST-memristor and two MOSFET transistors (p-type and n-type). A number of artificial synapses are employed to form a circuit which is expected to consume 2 − 8 % of the area compared to CMOS-only circuit. And the accuracy is about 80%, which is good enough in realistic diagnosis and has good robustness with noise.