The algorithms and techniques used to find useful patterns in large sets of data, collectively known as data mining and information visualization, have become vital to researchers making discoveries in diverse fields. The goals of data mining (and computing in general) at the Exascale have introduced fundamental challenges at the architectural level, and even though the evolution of GPU architecture has partly been driven by these challenges, overall computing system performance is not increasing at an equal rate as that of data generation and collection, thus widening the gap between the capabilities of mining algorithms and the performance of real-world data mining systems. This project investigates the design of new hardware/software platforms that will enable existing data mining algorithms to scale with increasingly large and complex datasets. In doing so, this project builds upon current understanding of the characteristics of data mining applications that differ from those for which modern processors are currently designed, similar to what already has been done in the signal processing and network processing domains. This project also studies a variety of design methodologies and models of computation that could lead to performance improvements. Finally, this project analyzes the inherent tradeoffs between algorithmic accuracy and architecture overhead in an attempt to generalize the accuracy and performance tradeoff. The expected impact is that these research tasks will contribute to the growing body of work in embedded system design at the hardware/software interface, and will help to develop a part of future hybrid multi-core computing platforms.