A recurrent crossbar of memristive nanodevices implements online novelty detection

Abstract

An auto-correlation matrix memory (ACMM) system continuously computes the degree to which a presented input is novel or anomalous relative to past examples. Here we demonstrate that such a filter can be efficiently implemented with memristive nanodevices and accompanying CMOS circuitry. Complete (a full crossbar) and incomplete (an array of memristive devices) variants of the proposed nanofabric are electrically detailed and subsequently simulated on a simple sparse input image test meant to gauge the system's responses to transitions. Both systems demonstrate active novelty filtering with a small level of false positives in the presence of noise, but only the complete system reports all transitions successfully (avoids false negative too). While the system is robust to a noisy channel, degradation towards false positives is more likely when nanodevice variability is taken into account as well. In addition to novelty filtering, the proposed system may be a useful building block for larger reservoir or recurrent on-chip learning systems.