A Spiking Neural Algorithm for Network Flow

dc.contributor.advisorKwisthout, J.H.P.
dc.contributor.advisorRooij van, Iris,
dc.contributor.authorAli, ABDULLAHI
dc.date.issued2019-06-05
dc.description.abstractIt is not clear what the potential is of neuromorphic hardware beyond machine learning and neuroscience. In this project, a problem is investigated that is inherently difficult to fully implement in neuromorphic hardware by introducing a new machine model in which a conventional Turing machine and neuromorphic oracle work together to solve such types of problems. A lattice of complexity classes is introduced: CSNN(RS), in which a neuromorphic oracle is consulted using only resources at most RS. We show that the P-complete MAX NETWORK FLOW problem is in LSNN(O(n);O(n);O(n)) for graphs with n edges. A modified variant of this algorithm is implemented on the Intel Loihi chip; a neuromorphic manycore processor developed by Intel Labs. We show that by off-loading the search for augmenting paths to the neuromorphic processor we can get energy efficiency gains, while not sacrificing runtime resources. This result demonstrates how P-complete problems can be mapped on neuromorphic architectures in a theoretically and potentially practically efficient manner.en_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10330
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleA Spiking Neural Algorithm for Network Flowen_US
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