Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/F5D189D4-4B5E-46BD-A6C5-AD0BCE220CC4 | ||
| Year | 2017 | ||
| Type of Item | Master Thesis | ||
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| Bibliographic Citation | Emmanouil 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, 2017 https://doi.org/10.26233/heallink.tuc.67517 | ||
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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.
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