Abstract:
Technological advancements in the recent past have seen to the rise of data availability as
a result of low cost storage devices and the advent of e-enabled systems in daily life. These
factors have played a pivotal role in enhancing the pace at which data mining and machine
learning have progressed. Data mining and machine learning algorithms have become an
integral part of major elds of engineering and sciences covering medical diagnostics, costumer
behavior and trend analysis, sentimental analysis, game playing, house hold, rescue
and other such robotics. The bottleneck in the application of data mining and machine
learning algorithms in every day task has been gap between the data growth and the performance
of algorithms. Various machine learning and data mining algorithms have inherent
parallelism which makes them a suitable target for parallel implementation. Parallel implementations
on hardware platforms can help in increasing the timing performance of the
algorithms.
In this dissertation, network on chip based application speci c architecture for data
mining applications have been proposed. We have targeted k-means and apriori algorithm
for parallel implementations. These algorithms are among the popular and widely used
algorithms in the eld of machine learning and data mining. We have proposed a multiple
elements based parallel framework for improved e ciency of these algorithms. The multiple
elements of the framework work in a collaborative environment where each element process
on independent data whereas they share their results with each other for completion of
iteration. While sharing of results with each other the traditional bus based connection
interfaces fail to provide a scalable interface. In order to enhance the scalability and
throughput of the proposed model, a Network-on-Chip (NoC) based interconnect model has
been integrated in the proposed multiple element framework. Furthermore, to e ectively
make use of the NoC platform an irregular NoC interconnect model is also proposed that
has been integrated with our proposed multiple element framework.
