An associative array architecture intended for semantic network processing

Abstract

This paper describes the organization and operation of a semantic network array processor (SNAP). The architecture consists of an array of identical cells each containing a content addressable memory, microprogram control and communication unit. Each cell is dedicated to one node of the semantic network and its associated relations. The array can perform global associative functions under the supervision of an outside controller. In addition, each cell is equipped with the necessary logic to perform individual functions. A set of primitive instructions were carefully chosen. Some of the applications discussed in this paper include pattern search operations, production systems and inferences. A LISP simulator was developed for this architecture, and some simulation results are presented in this paper.