NON-VONs Applicability to Three AI Task Areas

Abstract

NON-VON 1s a massively parallel machine constructed using custom VLSI chips, each containing a number of simple processing elements. A preliminary prototype is now operational at Columbia University . The machine is mtended to provide highly efficient support for a wide range of artificial intelligence and other symbolic applications . This paper briefly d es cribes the current verswn of the NON-VON machine and presents evidence for its applicability to the execution of OPS5 production systems, a number of low- and intermediate-level computer vision tasks, and certain "difficult" relational algebraic operations relevant to knowledge base management . Analytic and simulation results are presented for a number of algorithms . The data suggest that NON-VON could provide a performance improvement of as much as two to three orders of magnitude over a conventional sequential machine for a wide range of AI tasks.