Model for maximum crossbar size based on input driver impedance

Memristor crossbars are capable of implementing learning algorithms in a much more energy and area efficient manner compared with traditional systems. However, no results have been published that describe how large a neuromorphic memristor crossbar could theoretically become before it is inoperable. Furthermore, the input drivers to the crossbar are not typically studied in these neuromorphic system simulations. Presented is a model for determining the maximum memristor crossbar size relative to the input driver impedance. This is a powerful tool that can be used by a memristor system designers to quickly determine the trade-offs between maximum crossbar size, resistance, energy, and write speed. This information can then be used to determine how many independent crossbar circuits are required for a given neuromorphic application.