Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA
Proyecto de Graduación (Licenciatura en Ingeniería Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2017.
| Autor Principal: | Alfaro-Badilla, Jason kaled |
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| Otros Autores: | Chacón-Rodríguez, Alfonso |
| Formato: | Tesis |
| Idioma: | Español |
| Publicado: |
Instituto Tecnológico de Costa Rica
2019
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| Acceso en línea: |
https://hdl.handle.net/2238/10402 |
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RepoTEC10402 |
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RepoTEC104022023-10-05T21:19:09Z Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA Alfaro-Badilla, Jason kaled Chacón-Rodríguez, Alfonso Modelo Hodgkin-Huxley HLS Zynq-7000 ZedBoard Research Subject Categories::TECHNOLOGY::Electrical engineering, electronics and photonics::Electronics Proyecto de Graduación (Licenciatura en Ingeniería Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2017. An FPGA-synthesizable IP was implemented in order to compute a neural network using the extended Hodgkin-Huxley algorithm. An Euler aproximation algorithm written in the programming language C was used as input code, subject to synthesize the IP using the Vivado HLS Synthesis tool from Xilinx. The IP can work in parallel among several devices, so the computation is also parallelized, increasing thus the computational performance. The ZedBoard development board was used to test the generated IP. The software needed to manage the simulation programming initialization and data movement was implemented using di erent approaches: one using a bare metal implementation, and another using an embedded operating system. The interfaces used in the IP were the AXI4-Lite and AXI4-Stream, and a comparation of data transfer speeds was carried on. Also, some comparisons of performance between the implementation of the IP with the operative system and standalone system were carried on. Finally, the computational precision of the system was tested, using the C program as a reference. 2019-03-13T16:44:29Z 2019-03-13T16:44:29Z 2017 licentiateThesis https://hdl.handle.net/2238/10402 spa application/pdf Instituto Tecnológico de Costa Rica |
| institution |
Tecnológico de Costa Rica |
| collection |
Repositorio TEC |
| language |
Español |
| topic |
Modelo Hodgkin-Huxley HLS Zynq-7000 ZedBoard Research Subject Categories::TECHNOLOGY::Electrical engineering, electronics and photonics::Electronics |
| spellingShingle |
Modelo Hodgkin-Huxley HLS Zynq-7000 ZedBoard Research Subject Categories::TECHNOLOGY::Electrical engineering, electronics and photonics::Electronics Alfaro-Badilla, Jason kaled Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| description |
Proyecto de Graduación (Licenciatura en Ingeniería Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2017. |
| author2 |
Chacón-Rodríguez, Alfonso |
| format |
Tesis |
| author |
Alfaro-Badilla, Jason kaled |
| author_sort |
Alfaro-Badilla, Jason kaled |
| title |
Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| title_short |
Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| title_full |
Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| title_fullStr |
Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| title_full_unstemmed |
Diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-FPGA |
| title_sort |
diseño de un acelerador de hardware para simulaciones de redes neuronales biológicamente precisas utilizando un sistema multi-fpga |
| publisher |
Instituto Tecnológico de Costa Rica |
| publishDate |
2019 |
| url |
https://hdl.handle.net/2238/10402 |
| _version_ |
1796140686602403840 |
| score |
12.043282 |