Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/79802 |
Resumo: | In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a broad variety of research works on models based on point clouds, standing out for being efficient and robust in their intended tasks, but they are also characterized by requiring point cloud processing times greater than the minimum required, given the risky nature of the application. This research work aims to provide a design and implementation of a hardware IP optimized for computing convolutions, rectified linear unit (ReLU), padding, and max pooling. This engine was designed to enable the configuration of features such as varying the size of the feature map, filter size, stride, number of inputs, number of filters, and the number of hardware resources required for a specific convolution. Performance results show that by resorting to parallelism and quantization approach, the proposed solution could reduce the amount of logical FPGA resources by 40 to 50%, enhancing the processing time by 50% while maintaining the deep learning operation accuracy. |
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Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR dataconvolutional neural network (CNN)hardware acceleratorfield-programmable gate array (FPGA)light detection and ranging (LiDAR)quantizationobject detectionCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyIn recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a broad variety of research works on models based on point clouds, standing out for being efficient and robust in their intended tasks, but they are also characterized by requiring point cloud processing times greater than the minimum required, given the risky nature of the application. This research work aims to provide a design and implementation of a hardware IP optimized for computing convolutions, rectified linear unit (ReLU), padding, and max pooling. This engine was designed to enable the configuration of features such as varying the size of the feature map, filter size, stride, number of inputs, number of filters, and the number of hardware resources required for a specific convolution. Performance results show that by resorting to parallelism and quantization approach, the proposed solution could reduce the amount of logical FPGA resources by 40 to 50%, enhancing the processing time by 50% while maintaining the deep learning operation accuracy.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) (Project no. 037902; Funding Reference: POCI-01-0247-FEDER-037902)Multidisciplinary Digital Publishing InstituteUniversidade do MinhoSilva, João Pedro DuartePereira, Pedro Miguel CoelhoMachado, RuiNévoa, RafaelMelo-Pinto, PedroFernandes, Duarte2022-03-112022-03-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/79802engSilva, J.; Pereira, P.; Machado, R.; Névoa, R.; Melo-Pinto, P.; Fernandes, D. Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data. Sensors 2022, 22, 2184. https://doi.org/10.3390/s220621841424-82201424-822010.3390/s2206218435336357https://www.mdpi.com/1424-8220/22/6/2184info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:38:05Zoai:repositorium.sdum.uminho.pt:1822/79802Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:34:27.287642Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
title |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
spellingShingle |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data Silva, João Pedro Duarte convolutional neural network (CNN) hardware accelerator field-programmable gate array (FPGA) light detection and ranging (LiDAR) quantization object detection Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
title_short |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
title_full |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
title_fullStr |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
title_full_unstemmed |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
title_sort |
Customizable FPGA-based hardware accelerator for standard convolution processes empowered with quantization applied to LiDAR data |
author |
Silva, João Pedro Duarte |
author_facet |
Silva, João Pedro Duarte Pereira, Pedro Miguel Coelho Machado, Rui Névoa, Rafael Melo-Pinto, Pedro Fernandes, Duarte |
author_role |
author |
author2 |
Pereira, Pedro Miguel Coelho Machado, Rui Névoa, Rafael Melo-Pinto, Pedro Fernandes, Duarte |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, João Pedro Duarte Pereira, Pedro Miguel Coelho Machado, Rui Névoa, Rafael Melo-Pinto, Pedro Fernandes, Duarte |
dc.subject.por.fl_str_mv |
convolutional neural network (CNN) hardware accelerator field-programmable gate array (FPGA) light detection and ranging (LiDAR) quantization object detection Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
topic |
convolutional neural network (CNN) hardware accelerator field-programmable gate array (FPGA) light detection and ranging (LiDAR) quantization object detection Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
description |
In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a broad variety of research works on models based on point clouds, standing out for being efficient and robust in their intended tasks, but they are also characterized by requiring point cloud processing times greater than the minimum required, given the risky nature of the application. This research work aims to provide a design and implementation of a hardware IP optimized for computing convolutions, rectified linear unit (ReLU), padding, and max pooling. This engine was designed to enable the configuration of features such as varying the size of the feature map, filter size, stride, number of inputs, number of filters, and the number of hardware resources required for a specific convolution. Performance results show that by resorting to parallelism and quantization approach, the proposed solution could reduce the amount of logical FPGA resources by 40 to 50%, enhancing the processing time by 50% while maintaining the deep learning operation accuracy. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-11 2022-03-11T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/79802 |
url |
https://hdl.handle.net/1822/79802 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva, J.; Pereira, P.; Machado, R.; Névoa, R.; Melo-Pinto, P.; Fernandes, D. Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data. Sensors 2022, 22, 2184. https://doi.org/10.3390/s22062184 1424-8220 1424-8220 10.3390/s22062184 35336357 https://www.mdpi.com/1424-8220/22/6/2184 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132866516353024 |