This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Analysis of Accelerator Hardware for Autonomous Vehicles and Data Centers
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on October 22, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
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 The challenges for the infrastructure to incorporate AVs as a viable transportation source are immense, from an outdated infrastructure to radical Smart-City designs.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.
CitationBrown, K., "Analysis of Accelerator Hardware for Autonomous Vehicles and Data Centers," SAE Technical Paper 2019-01-2615, 2019.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Alvarez, M., Morales, J., and Kraak, M. , “Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications,” Sensors, 19 19(6):1372, Mar. 2019, doi:10.3390/s19061372.
- Lin, S., Zhang, Y. Hsu, C., Haquel, M., Tang, T., and Mars, J. , “The Architectural Implications of Autonomous Driving: Constraints and Acceleration,” in ASPLOS’18, March 24-28, 2018, Williamsburg, VA, Association for Computing Machinery, ACM ISBN 978-1-4503-4911-6/18/03.
- Eden Energy Institute , “Top 50 Smart City Governments,” https://static1.squarespace.com/static/5b3c517fec4eb767a04e73ff/t/5b513c57aa4a99f62d168e60/1532050650562/Eden-OXD_Top+50+Smart+City+Governments.pdf, accessed Jun. 2019.
- Schau, E., Traverso, M., and Finkbeiner, M. , “Life Cycle Approach to Sustainability Assessment: A Case Study of Remanufactured Alternators,” Journal of Remanufacturing 2(1):1-14, 2012.
- Farrington, R. and Rugh, J. , “Impact of Vehicle Air-Conditioning on Fuel Economy, Tailpipe Emissions, and Electric Vehicle Range,” Earth Technologies Forum 1-6, 2000.
- Audi USA , “2017 Audi A4 Ultra Offers Highest EPA-Estimated Fuel Economy in Competitive Segment,” 2017.
- Tesla, 2017 , “Full Self-Driving Hardware on All Cars,” https://www.tesla.com/autopilot, accessed Jul. 2019.
- Seagate Technology LLC , 2017, “Seagate Desktop HDD Specifications,” https://www.seagate.com/consumer/upgrade/desktop-hdd/%20/#specs, accessed Jul. 2019.
- Joudi, K., Mohammed, A., and Aljanabi, M. , “Experimental and Computer Performance Study of an Automotive Air Conditioning System with Alternative Refrigerants,” Energy conversion and Management 44(18):2959-2976, 2003.
- Chervolet , “Chevrolet Bolt EV,” https://www.chevrolet.com/%20bolt-ev-electric-vehicle, accessed Jul. 2019.
- Brown, M., Burschka, D., and Hager, G. , “Advances in Computational Stereo,” IEEE Trans. Pattern Analysis and Machine Intelligence 25(8):993-1008, 2003.
- Chelva, M. and Halse, S. , “A Performance Study of GPU, FPGA, DSP, and Multicore Processors,” ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), ISSN (PRINT) : 2320-8945, 3, 5, 6, 2015.
- Guo, K., Zeng, S., Yu, J., Wang, Y., and Yang, H. , “A Survey of FPGA-Based Neural Network Inference,” ACM Transactions on Reconfigurable Technology and Systems 9(4), Article 11.
- Hirst, J., Miller, J., Kaplan, B., and Reed, D. , “Watts Up? PRO AC Power Meter for Automated Energy Recording: A Product Review,” Behavior Analysis in Practice 6(1):82, 2013.
- Chen, Y., Emer, J., and Sze, V. , “Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks,” in Computer Architecture (ISCA), 2016 ACM/IEEE 43rd Annual International Symposium on, IEEE, 367-379.
- Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M., and Dally, W. , “EIE: Efficient Inference Engine on Compressed Deep Neural Network,” in Proceedings of the 43rd International Symposium on Computer Architecture, IEEE Press, 243-254.
- Zhang, C., Fang, Z., Zhou, P., Pan, P., and Cong, J. , “Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks,” in Computer-Aided Design (ICCAD), 2016 IEEE/ACM International Conference on, IEEE, 1-8.
- Tesla Inc. , “Tesla Autopilot: Full Self-Driving Hardware on All Cars,” https://www.tesla.com/autopilot, accessed Jul. 2019.
- Dustdar, S. and Shi, W. , “The Promise of Edge Computing,” The IEEE Computer Society, Computer IEEE Society, 0018-9162/16
- Caulfield, A. et al. “A Cloud-Scale Acceleration Architecture,” IEEE, 978-1-5090-3508-3/16.