Accelerating cogent confabulation: An exploration in the architecture design space

Qinru Qiu, Daniel Burns, Michael Moore, Richard Linderman, Thomas Renz, Qing Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software and hardware optimization. On the software implementation side, appropriate data structures can improve the performance of the software by more than 5,000X. On the hardware implementation side, the cogent confabulation algorithm is an ideal candidate for parallel processing and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost tradeoffs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve good cost performance tradeoffs.

Original languageEnglish (US)
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages1292-1300
Number of pages9
DOIs
StatePublished - Nov 24 2008
Externally publishedYes
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period6/1/086/8/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Qiu, Q., Burns, D., Moore, M., Linderman, R., Renz, T., & Wu, Q. (2008). Accelerating cogent confabulation: An exploration in the architecture design space. In 2008 International Joint Conference on Neural Networks, IJCNN 2008 (pp. 1292-1300). [4633965] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2008.4633965