The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
The Architectural Implications of Autonomous Driving: Constraints and Acceleration
Abstract
Autonomous driving systems have attracted a significant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla, and Mobileye, have invested a large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and finishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we first present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-offs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multicore CPUs are incapable of processing under the identified design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs, and ASICs, which can reduce the tail latency of the system by 169x, 10x, and 93x respectively. With accelerator-based designs, we are able to build an end-to-end autonomous driving system that meets all the design constraints, and explore the trade-offs among performance, power and the higher accuracy enabled by higher resolution cameras.
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Cites
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Cited by
Edge Computing for Autonomous Driving: Opportunities and Challenges
Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2915983 · Cited by 778
Mobile Edge Intelligence and Computing for the Internet of Vehicles
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Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
Doan, B. G., Abbasnejad, E., & Ranasinghe, D. C. (2020) · Annual Computer Security Applications Conference · doi:10.1145/3427228.3427264 · Cited by 263
Lwakatare, L. E., Raj, A., Crnkovic, I., Bosch, J., & Olsson, H. H. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106368 · Cited by 241
OpenEI: An Open Framework for Edge Intelligence
Zhang, X., Wang, Y., Lu, S., Liu, L., xu, L., & Shi, W. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00182 · Cited by 139
“explored the hardware computing platform design of autonomous vehicles [70].”
Background
Metadata
- Authors
- Lin, S.-C., Zhang, Y., Hsu, C.-H., Skach, M., Haque, M. E., Tang, L., & Mars, J.
- Venue
- Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems
- Publisher
- ACM
- Published
- 2018-03-19
- Pages
- 751-766
- OpenAlex
- W2791175987
- Cited by
- 367
- References
- 37
- FWCI
- 42.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, S.-C., Zhang, Y., Hsu, C.-H., Skach, M., Haque, M. E., Tang, L., & Mars, J. (2018). The Architectural Implications of Autonomous Driving: Constraints and Acceleration. Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems. https://doi.org/10.1145/3173162.3173191
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