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Design of a clustered multiprocessor for real-time natural language understanding

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2 Author(s)
DeMara, R.F. ; Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA ; Moldovan, D.I.

The authors have designed a parallel architecture called the semantic network array processor (SNAP) for natural language understanding (NLU) and other artificial intelligence applications. It is capable of executing large marker-passing programs and generating results in real-time. The design features 32 processing clusters with four to five functionally dedicated digital signal processors in each cluster. Processors within clusters share a marker-processing memory while communication between clusters is implemented by a buffered message-passing scheme. Throughout the machine, overlapping groups of multiport memories provide a direct yet visible interconnection network. The result is a low cost, flexible, and observable parallel processor capable of performing NLU operations within subsecond response time

Published in:

Parallel Processing Symposium, 1991. Proceedings., Fifth International

Date of Conference:

30 Apr-2 May 1991