Date of Award

8-2009

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Computer Engineering

Advisor

Taha, Tarek M

Committee Member

Ligon , Walter B

Committee Member

Smotherman , Mark

Abstract

The human cortex is the seat of learning and cognition. Biological scale
implementations of cortical models have the potential to provide significantly more
power problem solving capabilities than traditional computing algorithms. The large
scale implementation and design of these models has attracted significant attention
recently. High performance implementations of the models are needed to enable such
large scale designs. This thesis examines the acceleration of the spiking neural network
class of cortical models on several modern multicore processors. These include the
Izhikevich, Wilson, Morris-Lecar, and Hodgkin-Huxley models. The architectures
examined are the STI Cell, Sun UltraSPARC T2+, and Intel Xeon E5345. Results
indicate that these modern multicore processors can provide significant speed-ups and
thus are useful in developing large scale cortical models.
The models are then implemented on a 50 TeraFLOPS 336 node PlayStation 3
cluster. Results indicate that the models scale well on this cluster and can emulate 108
neurons and 1010 synapses. These numbers are comparable to the large scale cortical
model implementation studies performed by IBM using the Blue Gene/L supercomputer.
This study indicates that a cluster of PlayStation 3s can provide an economical, yet
powerful, platform for simulating large scale biological models.

Share

COinS