Publications

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Final report for Cognitive Computing for Security LDRD 165613. It reports on the development of hybrid of general purpose/ne uromorphic computer architecture, with an emphasis on potential implementation with memristors.

Some next generation computing devices may consist of resistive memory arranged as a crossbar. Currently, the dominant approach is to use crossbars as the weight matrix of a neural network, and to use learning algorithms that require small incremental weight updates, such as gradient descent (for example Backpropagation). Using real-world measurements, we demonstrate that resistive memory devices are unlikely to support such learning methods. As an alternative, we offer a random search algorithm tailored to the measured characteristics of our devices.

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This report discusses aspects of neuromorphic computing and how it is used to model microsystems.

Current knowledge of memristor behavior is limited to a few physical models of which little comprehensive data collection has taken place. The purpose of this research is to collect data in search of exploitable memristor behavior by designing and implementing tests on a HP Labs Rev2 Memristor Test Board. The results are then graphed in their optimal format for conceptualizing behavioral patterns. This series of experiments has concluded the existence of an additional memristor state affecting the behavior of memristors when pulsed with positively polarized DC voltages. This effect has been observed across multiple memristors and data sets. The following pages outline the process that led to the hypothetical existence and eventual proof of this additional state of memristor behavior.