Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications
In the rapidly evolving field of autonomous cars, advanced deep learning systems have ushered in a new era of innovation, enabling the integration of unique features into vehicles. These advancements span various areas, including pedestrian and vehicle detection, recognition of road signs and drivin...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10776987/ |
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| author | Mohammad Cheshfar Mohammad Hossein Maghami Parviz Amiri Hossein Gharaee Garakani Luciano Lavagno |
| author_facet | Mohammad Cheshfar Mohammad Hossein Maghami Parviz Amiri Hossein Gharaee Garakani Luciano Lavagno |
| author_sort | Mohammad Cheshfar |
| collection | DOAJ |
| description | In the rapidly evolving field of autonomous cars, advanced deep learning systems have ushered in a new era of innovation, enabling the integration of unique features into vehicles. These advancements span various areas, including pedestrian and vehicle detection, recognition of road signs and driving patterns, identification of drivable roads and scenes, and improved mapping and routing techniques. However, the high computational requirements of deep learning networks present a significant challenge, especially for embedded systems like FPGAs (Field-Programmable Gate Arrays) that have limited capacity. Addressing this challenge, this article presents a comprehensive survey of the methodologies employed in implementing Convolutional Neural Networks (CNNs) on resource-constrained processors, within the domain of self-driving car applications. Our survey encompasses a thorough review of the existing literature in the field of deep learning applied to autonomous cars, from perception to localization, with a specific emphasis on implementations utilizing embedded hardware such as FPGAs and embedded GPUs. Furthermore, we present and analyze results that elucidate the intricate trade-offs between latency, energy consumption, and the judicious selection of the underlying platform. These insights are crucial for researchers and practitioners in the field, as they provide a clear direction for optimizing the performance of deep learning networks on resource-constrained platforms, ultimately contributing to the advancement of self-driving car technologies. |
| format | Article |
| id | doaj-art-ea4b123e7dbd41c2b7d0f837c5f14c6b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ea4b123e7dbd41c2b7d0f837c5f14c6b2024-12-11T00:05:59ZengIEEEIEEE Access2169-35362024-01-011218241018243710.1109/ACCESS.2024.351067710776987Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars ApplicationsMohammad Cheshfar0https://orcid.org/0000-0003-1464-0773Mohammad Hossein Maghami1https://orcid.org/0000-0002-7932-9161Parviz Amiri2https://orcid.org/0000-0001-5764-0912Hossein Gharaee Garakani3https://orcid.org/0000-0002-9905-6156Luciano Lavagno4https://orcid.org/0000-0002-9762-6522Department of Electronics and Telecommunications, Politecnico di Torino, Turin, ItalyFaculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranFaculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, IranDepartment of Information Technology, ICT Research Institute, Tehran, IranDepartment of Electronics and Telecommunications, Politecnico di Torino, Turin, ItalyIn the rapidly evolving field of autonomous cars, advanced deep learning systems have ushered in a new era of innovation, enabling the integration of unique features into vehicles. These advancements span various areas, including pedestrian and vehicle detection, recognition of road signs and driving patterns, identification of drivable roads and scenes, and improved mapping and routing techniques. However, the high computational requirements of deep learning networks present a significant challenge, especially for embedded systems like FPGAs (Field-Programmable Gate Arrays) that have limited capacity. Addressing this challenge, this article presents a comprehensive survey of the methodologies employed in implementing Convolutional Neural Networks (CNNs) on resource-constrained processors, within the domain of self-driving car applications. Our survey encompasses a thorough review of the existing literature in the field of deep learning applied to autonomous cars, from perception to localization, with a specific emphasis on implementations utilizing embedded hardware such as FPGAs and embedded GPUs. Furthermore, we present and analyze results that elucidate the intricate trade-offs between latency, energy consumption, and the judicious selection of the underlying platform. These insights are crucial for researchers and practitioners in the field, as they provide a clear direction for optimizing the performance of deep learning networks on resource-constrained platforms, ultimately contributing to the advancement of self-driving car technologies.https://ieeexplore.ieee.org/document/10776987/Hardware acceleratorFPGAautonomous carconvolutional neural network |
| spellingShingle | Mohammad Cheshfar Mohammad Hossein Maghami Parviz Amiri Hossein Gharaee Garakani Luciano Lavagno Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications IEEE Access Hardware accelerator FPGA autonomous car convolutional neural network |
| title | Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications |
| title_full | Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications |
| title_fullStr | Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications |
| title_full_unstemmed | Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications |
| title_short | Comparative Survey of Embedded System Implementations of Convolutional Neural Networks in Autonomous Cars Applications |
| title_sort | comparative survey of embedded system implementations of convolutional neural networks in autonomous cars applications |
| topic | Hardware accelerator FPGA autonomous car convolutional neural network |
| url | https://ieeexplore.ieee.org/document/10776987/ |
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