Abstract
Memristor crossbars are capable of implementing learning algorithms in a much more energy- and area-efficient manner compared with traditional systems. However, the programmable nature of memristor crossbars must first be explored on a smaller scale to see if physical devices are suitable for applications of reconfigurable computing. The reconfigurability of these devices through small scale memristor crossbar implementations is demonstrated. It is shown that a crossbar containing eight memristors is capable of learning several different two-input Boolean logic functions. A strong foundation is provided to build on demonstrating that physical memristor crossbars can be programmed as linear classifiers.
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