Cognitive domain ontologies in a memristor crossbar architecture

Abstract

Cognitive agents are typically utilized in autonomous systems for automated decision making. These systems interact at real time with their environment and are generally heavily power constrained. Thus, there is a strong need for a real time agent running on a low power platform. This paper examines how some of the components of a cognitive agent can be executed in memristor crossbar circuits capable of highly parallel instruction execution. The agent examined is the Cognitively Enhanced Complex Event Processing (CECEP) architecture. This is an autonomous decision support tool that reasons like humans and enables enhanced agent-based decision-making. It has applications in a large variety of domains including autonomous systems, operations research, intelligence analysis, and data mining. One of the most time consuming and key components of CECEP is the mining of knowledge from a repository described as a Cognitive Domain Ontology (CDO). We show that CDOs can be implemented using memristor crossbars using two different approaches. The first is a lookup table approach that is stored in a high density memristor matching circuit. The second in a multilayer perceptron implementation that uses an ex-situ memristor based neuromorphic system. In each case, the example CDOs are implemented successfully.