This research investigates the use of Artificial Neural Networks (ANNs) to predict first year student retention rates. Based on a significant body of previous research, this work expands on previous attempts to predict student outcomes using machinelearning techniques. Using a large data set provided by Columbus State University's Information Technology department, ANNs were used to analyze incoming first-year traditional freshmen students' data over a period from 2005-2011. Using several different network designs, the students' data was analyzed, and a basic predictive network was devised. While the overall accuracy was high when including the first and second semesters worth of data, once the data set was reduced to a single semester, the overall accuracy dropped significantly. Using different network designs, more complex learning algorithms, and better training strategies, the prediction accuracy rate for a student's return to the second year approached 75% overall. Since the rate is still low, there is room for improvements, and several techniques that might increase the reliability of these networks are discussed.

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Neuromorphic computing is a broad category of non–von Neumann architectures that mimic biological nervous systems using hardware. Current research shows that this class of computing can execute data classification algorithms using only a tiny fraction of the power conventional CPUs require. This raises the larger research question: How might neuromorphic computing be used to improve application performance, power consumption, and overall system reliability of future supercomputers? To address this question, an open-source neuromorphic processor architecture simulator called NeMo is being developed. This effort will enable the design space exploration of potential heterogeneous compute systems that combine traditional CPUs, GPUs, and neuromorphic hardware. This article examines the design, implementation, and performance of NeMo . Demonstration of NeMo ’s efficient execution using 2,048 nodes of an IBM Blue Gene/Q system, modeling 8,388,608 neuromorphic processing cores is reported. The peak performance of NeMo is just over ten billion events-per-second when operating at this scale.

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