Parallelizing two classes of neuromorphic models on the cell multicore architecture

Authors:
Tarek M. Taha;Pavan Yalamanchili;Mohammad A. Bhuiyan;Rommel Jalasutram;Sumod K. Mohan
Affiliations:
Electrical and Computer Engineering Department, Clemson University, Clemson, SC;Electrical and Computer Engineering Department, Clemson University, Clemson, SC;Electrical and Computer Engineering Department, Clemson University, Clemson, SC;Electrical and Computer Engineering Department, Clemson University, Clemson, SC;Electrical and Computer Engineering Department, Clemson University, Clemson, SC
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

There is a significant interest in the research community to develop large scale, high performance implementations of cortical models. These have the potential to provide significantly stronger information processing capabilities than current computing algorithms. At present we are investigating the implementation of six neuromorphic computational models on a large Sony PlayStation 3 cluster at the Air Force research lab. These six models span two classes of neuromorphic algorithms: hierarchical Bayesian and spiking neural networks. In this paper we have presented the performance gain of these six neuromorphic computational models for implementations on the Cell multicore processor on the PlayStation 3. We show that the Cell multicore architecture can provide significant performance gains for these models. We compare the performance gains of the two classes and see that the hierarchical Bayesian class provides higher speedups than the spiking network class in general. This is primarily due to the higher computational load per node in the former class. Our results indicate that the Cell processor based PlayStation 3 would provide a good platform for the large scale implementation of the classes of neuromorphic models examined.