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Use este identificador para citar ou linkar para este item: http://repositorio.unb.br/handle/10482/39986
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Título: A manycore vision processor architecture for embedded applications
Autor(es): Silva, Bruno Almeida da
Lima, Arthur Mendes
Silva, Jones Yudi Mori Alves da
Assunto: MPSoC
NoC
Processamento de imagens
Visão por computador
Data de publicação: 2020
Editora: IEEE
Referência: SILVA, Bruno Almeida da; LIMA, Arthur Mendes; YUDI, Jones. A manycore vision processor architecture for embedded applications. In: BRAZILIAN SYMPOSIUM ON COMPUTING SYSTEMS ENGINEERING (SBESC), 10., Florianópolis, 2020. p. 1-8. DOI: 10.1109/SBESC51047.2020.9277867. Disponível em: https://ieeexplore.ieee.org/document/9277867.
Abstract: Real-Time Image Processing and Computer Vision systems are now in the mainstream of technologies enabling applications for Cyber-Physical Systems, Internet of Things, Augmented Reality, and Industry 4.0. These applications bring the need for Smart Camera for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a many-core vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed Processing Elements and Memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to four hundred Processing Elements were implemented and compared to several works from the literature. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.
Unidade Acadêmica: Faculdade de Tecnologia (FT)
Departamento de Engenharia Mecânica (FT ENM)
DOI: 10.1109/SBESC51047.2020.9277867
Versão da editora: https://ieeexplore.ieee.org/document/9277867
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