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Genetic Algorithm Optimization Of Experiment Design For Targeted Uncertainty Reduction, Alexander Amedeo Depillis
Genetic Algorithm Optimization Of Experiment Design For Targeted Uncertainty Reduction, Alexander Amedeo Depillis
Masters Theses
Nuclear cross sections are a set of parameters that capture probability information about various nuclear reactions. Nuclear cross section data must be experimentally measured, and this results in simulations with nuclear data-induced uncertainties on simulation outputs. This nuclear data-induced uncertainty on most parameters of interest can be reduced by adjusting the nuclear data based on the results from an experiment. Integral nuclear experiments are experiments where the results are related to many different cross sections. Nuclear data may be adjusted to have less uncertainty by adjusting them to match the results obtained from integral experiments. Different integral experiments will adjust …
Mapping Arbitrary Spiking Neural Networks To The Ravens Neuroprocessor, Jongheon Park
Mapping Arbitrary Spiking Neural Networks To The Ravens Neuroprocessor, Jongheon Park
Masters Theses
In neuromorphic computing, a hardware implementation of a spiking neural network is used to provide improved speed and power efficiency over simulations of the networks on a traditional Von Neumann architecture. These hardware implementations employ bio-inspired architecture usually consisting of artificial neurons and synapses implemented in either analog, digital, or mixed-signal circuits. Since these hardware spiking neural networks are designed to support arbitrary networks under the constraints imposed by the available hardware resource, they have to be programmed by off-chip software with awareness of those constraints. The TENNLab research group at the University of Tennessee, Knoxville has recently developed the …