while using set on selected cells. In this case, for a 3D-ReRAM 1TNR array, with a column of N ReRAM cells situated above a select transistor, only the intrinsic nonlinearity of the HRS is required to be sufficiently large, since the number of vertical levels N is limited (e.g., N = 8–32), and this has been shown possible for a low-current ReRAM system. Modeling of 2D and 3D caches designed with ReRAM and other non-volatile random access memories such as MRAM and PCM can be done using DESTINY tool. Proposed role in artificial intelligence applications The increasing computational demands necessary for many improvements in artificial intelligence have led many to speculate that ReRAM implementations could be extremely useful hardware for running artificial intelligence and machine learning applications. Researchers at School of Engineering of Stanford University Havel built up a RRAM that "does the AI processing within the memory itself, thereby eliminating the separation between the compute and memory units." It is twice as energy efficient as state-of-the-art. References Solid-state computer storage media Computer memory Non-volatile random-access memory Emerging technologies Types of RAM