Mostrar el registro sencillo del ítem
Design of a multi-FPGA system for biologically plausible neural networks based of heterogeneous computing
dc.contributor.advisor | Chacón-Rodríguez, Alfonso | es |
dc.contributor.author | Alfaro-Badilla, Jason Kaled | |
dc.date.accessioned | 2023-03-21T17:41:47Z | |
dc.date.available | 2023-03-21T17:41:47Z | |
dc.date.issued | 2022-08-31 | |
dc.identifier.uri | https://hdl.handle.net/2238/14258 | |
dc.description | Proyecto de Graduación (Maestría en Electrónica), Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2022 | es |
dc.description.abstract | Today neuroscience is vastly specialized such that computational neuroscience tries to bridge the gaps of knowledge between the theory and the experiments. In-silico experiments are computer simulations with complete control over the scenario; this techniques try to decode the functionality of the biological neural networks and the biophysical dynamics which this cells inherent. This work explores a way to improve biological-precise spiking neural networks simulations with FPGA acceleration. Our approach focuses with creating a hardware acceleration for one cell compartment using a system-on-chip, this serves a proof-of-concept to value how flexible is the platform to accelerate similar simulations using the hybrid hardware-software methods. The work described in this thesis is a implementation of the inferior olivary nuclei model implemented with a extended Hodgkin-Huxley neural model. The development platform was the Xilinx’s Zynq-7000 and the Vivado Hardware Design suite. Results obtained in this work shows that the hybrid computing is more performance efficient in using the FPGA resources. Also proves a more flexible platform unlike other authors similar work. Finally, the use of a shared DRAM between the CPU and FPGA fabric showed a bottleneck for the design, its noted that it would be preferable to separate if possible the main DRAM between both systems. | es |
dc.language.iso | eng | es |
dc.publisher | Instituto Tecnológico de Costa Rica | es |
dc.rights | acceso abierto | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | SNN | es |
dc.subject | SoC | es |
dc.subject | Neural network | es |
dc.subject | Hodgkin-Huxley | es |
dc.subject | Cluster | es |
dc.subject | HPC | es |
dc.subject | Diseño de sistemas | es |
dc.subject | Multi-FPGA | es |
dc.subject | Redes neuronales | es |
dc.subject | Clúster | es |
dc.subject | System design | es |
dc.subject | Neurociencia | es |
dc.subject | Neuroscience | es |
dc.subject | Research Subject Categories::TECHNOLOGY::Electrical engineering, electronics and photonics | es |
dc.title | Design of a multi-FPGA system for biologically plausible neural networks based of heterogeneous computing | es |
dc.type | tesis de maestría | es |