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Analysis of Accelerator Hardware for Autonomous Vehicles and Data Centers
Technical Paper
2019-01-2615
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The development of Autonomous Vehicles (AV) has become a popular subject in academia and industry. Companies and cities are quickly realizing the opportunities that AVs can generate from Mobility as a Service to traffic safety. The challenges for the infrastructure to incorporate AVs as a viable transportation source are immense, from an outdated infrastructure to radical Smart-City designs. Historically, the transportation infrastructure has faced challenges from underfunding, economics, and much needed improvements. With the current infrastructure unable to support many of the services required by a fully connected network, a transformation will be necessary to meet growing mobility needs. The role of accelerating technology in data centers are key for production operations among industry leaders such as Amazon and Microsoft for real-time processing. The same accelerating technology that has successfully impacted data centers will play the same role in much smaller micro data centers (mDC) for Smart-City design in the transportation infrastructure. These mDCs and Edge computing sites will be tasked with the latency, tasking caching and offloading (TCO), and processing of millions of connected vehicles simultaneously. With the recent upgrade of 5G from 4G wireless connectivity will invariably provide lower latency to Edge computing devices used in real-time applications. This paper provides an analysis of accelerator technology for real-time processing in the transportation infrastructure. Accelerator hardware such as FPGAs, GPUs, and ACISs will be highlighted from current research that support real-time capabilities. As the popularity of AVs and a connected network continues to grow, the role of accelerator technology will enable large scale real-time processing in AVs and the transportation infrastructure.
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Citation
Brown, K., "Analysis of Accelerator Hardware for Autonomous Vehicles and Data Centers," SAE Technical Paper 2019-01-2615, 2019, https://doi.org/10.4271/2019-01-2615.Data Sets - Support Documents
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