A note on associative processors for data management
ACM Transactions on Database Systems (TODS)
Concepts and capabilities of a database computer\
ACM Transactions on Database Systems (TODS)
The design of a rotating associative memory for relational database applications
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Structured Computer Vision; Machine Perception through Hierarchical Computation Structures
Structured Computer Vision; Machine Perception through Hierarchical Computation Structures
Logic and Data Bases
The architecture of CASSM: A cellular system for non-numeric processing
ISCA '73 Proceedings of the 1st annual symposium on Computer architecture
The Connection Machine
Knowledge-based retrieval on a relational database machine
Knowledge-based retrieval on a relational database machine
Image understanding algorithms on fine-grained tree-structured simd machines (computer vision, parallel architectures)
Knowledge and Database Management
IEEE Software
Direct A Multiprocessor Organization for Supporting Relational Database Management Systems
IEEE Transactions on Computers
IEEE Transactions on Computers
PASM: A Partitionable SIMD/MIMD System for Image Processing and Pattern Recognition
IEEE Transactions on Computers
Performance Analysis of Alternative Database Machine Architectures
IEEE Transactions on Software Engineering
Computer
A computer oriented toward spatial problems
IRE-ACM-AIEE '58 (Western) Proceedings of the May 6-8, 1958, western joint computer conference: contrasts in computers
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NON-VON is 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 intended to provide highly efficient support for a wide range of artificial intelligence and other symbolic applications. This paper briefly describes the current version of the NONVON 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.