Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Committee Chair/Advisor

Taha, Tarek

Committee Member

Birchfield , Stanley

Committee Member

Hoover , Adam


There is a significant interest in the research community to develop large scale,
high performance implementations of neuromorphic models. These have the potential to
provide significantly stronger information processing capabilities than current computing
algorithms. This thesis examines the parallelization of two recent biologically inspired
hierarchical Bayesian cortical models onto recent multicore architectures. These models
have been developed recently based on new insights from neuroscience and have several
advantages over traditional neural networks. In particular, they need far fewer network
nodes to simulate a large scale cortical model than traditional neural networks, making
them computationally more efficient. This is the first study of the parallelization of this
class of models onto multicore processors. Results indicate that the models can take
advantage of parallelism present in the processors to provide significant speedups on
multicore architectures. These models are further shown to scale well on a cluster of 336
PS3s available at the Air Force Research Lab which is shown to emulate between 10E8 to
1010 neurons. In particular, the results indicate that a cluster of Playstation 3s can provide
an economical, yet powerful, platform for simulating large scale neuromorphic models.



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