Evaluation of the Implications of Nanoscale Architectures on Contextual Knowledge Discovery and Memory: Self-Assembled Architectures and Memory

Abstract : Computing systems with advanced situational awareness and the ability to use contextual knowledge to interpret sensor data have the potential to be instrumental in many contexts. This project developed three systems to query a database with immense numbers of objects and rich sets of contextual relationships. In particular, large-scale content addressable memory systems provide a better solution to the knowledge discovery problem than conventional general-purpose memory systems. This project studied three systems: 1) a conventional system, 2) a conventional system optimized for online (i.e. real-time) use, and 3) a novel DNA self assembled nanoelectronic system. The project developed tools for DNA self-assembly to provide simulation capabilities for evaluating the three systems and the data has shown that significant performance enhancements can be achieved by optimization. Further, when self-assembling technologies mature they will be able to achieve greater performance due to the massive parallelism inherent in the knowledge discovery problem.

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