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An architecture for the acceleration of a hybrid leaky integrate and fire SNN on the convey HC-2ex FPGA-based processor

Kousanakis Emmanouil, Dollas Apostolos, Sotiriadis Evripidis, Papaefstathiou Ioannis, Pnevmatikatos Dionysios, Papoutsi Athanasia, Πετραντωνάκης Παναγιώτης Κ., Poirazi Panagiota, Χαυλής Σπυρίδων, Καστελλάκης Γεώργιος

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/1F252B03-5DC5-4B71-8294-C9E461BCEB0E-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/7966649/-
Αναγνωριστικόhttps://doi.org/10.1109/FCCM.2017.51-
Γλώσσαen-
Μέγεθος8 pagesen
ΤίτλοςAn architecture for the acceleration of a hybrid leaky integrate and fire SNN on the convey HC-2ex FPGA-based processoren
ΔημιουργόςKousanakis Emmanouilen
ΔημιουργόςΚουσανακης Εμμανουηλel
ΔημιουργόςDollas Apostolosen
ΔημιουργόςΔολλας Αποστολοςel
ΔημιουργόςSotiriadis Evripidisen
ΔημιουργόςΣωτηριαδης Ευριπιδηςel
ΔημιουργόςPapaefstathiou Ioannisen
ΔημιουργόςΠαπαευσταθιου Ιωαννηςel
ΔημιουργόςPnevmatikatos Dionysiosen
ΔημιουργόςΠνευματικατος Διονυσιοςel
ΔημιουργόςPapoutsi Athanasiaen
ΔημιουργόςΠαπουτση Αθανασιαel
ΔημιουργόςΠετραντωνάκης Παναγιώτης Κ.el
ΔημιουργόςPetrantonakis Panagiotis C.en
ΔημιουργόςPoirazi Panagiotaen
ΔημιουργόςΠοιραζη Παναγιωταel
ΔημιουργόςΧαυλής Σπυρίδωνel
ΔημιουργόςChavlis Spyridonen
ΔημιουργόςΚαστελλάκης Γεώργιοςel
ΔημιουργόςKastellakis Georgeen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηNeuromorphic computing is expanding by leaps and bounds through custom integrated circuits (digital and analog), and large scale platforms developed by industry or government-funded 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 highly accurate neuromorphic computing 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 923x 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 computers 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-05-08-
Ημερομηνία Δημοσίευσης2017-
Θεματική ΚατηγορίαBCM ruleen
Θεματική ΚατηγορίαFPGAen
Θεματική ΚατηγορίαHomeostatic plasticityen
Θεματική ΚατηγορίαLeaky integrate and fire modelen
Θεματική ΚατηγορίαSimulation speedupen
Θεματική ΚατηγορίαSpiking neural networksen
Βιβλιογραφική ΑναφοράE. Kousanakis, A. Dollas, E. Sotiriades, I. Papaefstathiou, D. N. Pnevmatikatos, A. Papoutsi, P. C. Petrantonakis, P. Poirazi, S. Chavlis and G. Kastellakis, "An architecture for the acceleration of a hybrid leaky integrate and fire SNN on the convey HC-2ex FPGA-based processor," in 25th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, 2017, pp. 56-63. doi: 10.1109/FCCM.2017.51 en

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