Επεξεργαστής για προσομοίωση νευροβιολογικών διεργασιών βασισμένος σε αναδιατασσόμενη λογική (EL)

Reconfigurable Logic-Based Processor for the Simulation of Neurobiological Processes (EN)

Κουσανακης Εμμανουηλ (EL)

Kousanakis Emmanouil (EN)

Πνευματικατος Διονυσιος (EL)

Ποιραζη Παναγιωτα (EL)

Πολυτεχνείο Κρήτης (EL)

Δολλας Αποστολος (EL)

Dollas Apostolos (EN)

Technical University of Crete (EN)

Poirazi Panagiota (EN)

Pnevmatikatos Dionysios (EN)

Neuromorphic 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)

masterThesis

simulation speedup (EN)

Convey HC-2ex (EN)

FPGA (EN)

Spiking neural networks (EN)

Reconfigurable logic (EN)

Leacky integrate and fire model (EN)

Πολυτεχνείο Κρήτης

Ιδρυματικό Αποθετήριο Πολυτεχνείου Κρήτης

School of Electrical and Computer Engineering - Diploma Works

Intelligent Systems Laboratory - Diploma Works

Αγγλική γλώσσα

2017

http://purl.tuc.gr/dl/dias/F5D189D4-4B5E-46BD-A6C5-AD0BCE220CC4

Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών (EL)

Technical University of Crete::School of Electrical and Computer Engineering (EN)