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Reconfigurable logic-based processor for the simulation of neurobiological processes

Kousanakis Emmanouil

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URIhttp://purl.tuc.gr/dl/dias/F5D189D4-4B5E-46BD-A6C5-AD0BCE220CC4-
Identifierhttps://doi.org/10.26233/heallink.tuc.67517-
Languageen-
Extent82 pagesen
TitleΕπεξεργαστής για προσομοίωση νευροβιολογικών διεργασιών βασισμένος σε αναδιατασσόμενη λογικήel
TitleReconfigurable logic-based processor for the simulation of neurobiological processesen
CreatorKousanakis Emmanouilen
CreatorΚουσανακης Εμμανουηλel
Contributor [Thesis Supervisor]Dollas Apostolosen
Contributor [Thesis Supervisor]Δολλας Αποστολοςel
Contributor [Committee Member]Pnevmatikatos Dionysiosen
Contributor [Committee Member]Πνευματικατος Διονυσιοςel
Contributor [Committee Member]Poirazi Panagiotaen
Contributor [Committee Member]Ποιραζη Παναγιωταel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryNeuromorphic computing is expanding by leaps and bounds through custom integrated circuits (both digital and analog), and large scale platforms developed by industry and by government funded large projects (e.g. TrueNorth and BrainScaleS, respectively). Whereas the trend is for massive parallelism and neuromorphic computation in order to solve problems, such as those that may appear in machine learning and deep learning algorithms, there is substantial work on brain-like neuromorphic computing with a high degree of precision and accuracy, in order to model the human brain. In such a form of computing, spiking neural networks (SNN) such as the Hodgkin and Huxley model are mapped to various technologies, including FPGAs. In this work, we present a highly efficient FPGA-based architecture for the detailed hybrid Leaky Integrate and Fire SNN that can simulate generic characteristics of neurons of the cerebral cortex. This architecture supports arbitrary, sparse O(n2) interconnection of neurons without need to re-compile the design, and plasticity rules, yielding on a four-FPGA Convey 2ex hybrid computer a speedup of 823x for a non-trivial data set on 240 neurons vs. the same model in the software simulator BRAIN on a Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz, i.e. the reference state-of-the-art software. Although the reference, official software is single core, the speedup demonstrates that the application scales well among multiple FPGAs, whereas this would not be the case in general-purpose computing approaches due to the arbitrary interconnect requirements. The FPGA-based approach leads to highly detailed models of parts of the human brain up to a few hundred neurons vs. a dozen or fewer neurons on the reference system.en
Type of ItemΜεταπτυχιακή Διατριβήel
Type of ItemMaster Thesisen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2017-03-08-
Date of Publication2017-
SubjectSimulation speedupen
SubjectLeacky integrate and fire modelen
SubjectSpiking neural networksen
SubjectConvey HC-2exen
SubjectReconfigurable logicen
SubjectFPGAen
Bibliographic CitationEmmanouil Kousanakis, "Reconfigurable Logic-Based Processor for the Simulation of Neurobiological Processes", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2017en
Bibliographic CitationΕμμανουήλ Κουσανάκης, "Επεξεργαστής για προσομοίωση νευροβιολογικών διεργασιών βασισμένος σε αναδιατασσόμενη λογική", Μεταπτυχιακή Διατριβή, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2017el

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