Parallel knowledge processing on SNAP

Author

Moldovan, Dan I. ; Lee, Wing ; Lin, Changhwa

Author_Institution

Dept. of Electr. Eng.-Syst., Univ. of Southern California, Los Angeles, CA, USA

Volume

5

Issue

1

fYear

1993

fDate

2/1/1993 12:00:00 AM

Firstpage

65

Lastpage

75

Abstract

The semantic network array processor (SNAP) is a specialized, highly parallel architecture for knowledge representation and reasoning. The instruction set has been carefully designed to reflect the requirements of semantic network processing. SNAP is a marker propagation architecture, where the passing of markers between cells plays a fundamental role. The movement of markers between cells is controlled by a set of propagation rules. Various reasoning mechanisms were implemented using these propagation rules. A simulator was developed, and knowledge processing examples, such as inheritance, recognition, and classification, were tested. By comparing the simulation results with the same examples run on the Connection Machine, it was found that SNAP outperforms the Connection Machine over a broad range of knowledge processing examples by a factor of 1000 or more

Keywords

digital simulation; instruction sets; parallel architectures; semantic networks; Connection Machine; classification; highly parallel architecture; inheritance; instruction set; knowledge representation; marker propagation architecture; markers; reasoning; reasoning mechanisms; recognition; semantic network array processor; simulator; Array signal processing; Artificial intelligence; Hardware; Helium; Humans; Knowledge representation; Natural languages; Parallel architectures; Parallel languages; Testing;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.204092

Filename

204092