Driving into the memory wall - the role of memory for advanced driver assistance systems and autonomous driving
| Autor: | Chirag Sudarshan, Norbert Wehn, Matthias Jung, Christoph Dropmann, Christian Weis, Sally A. McKee |
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| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
business.industry Computer science Control unit Automotive industry Advanced driver assistance systems Memory bandwidth 02 engineering and technology Sensor fusion 01 natural sciences 020202 computer hardware & architecture Memory wall Embedded system 0103 physical sciences 0202 electrical engineering electronic engineering information engineering business Dram Consumer market |
| Zdroj: | Proceedings of the International Symposium on Memory Systems-MEMSYS 18 Proceedings of the International Symposium on Memory Systems -MEMSYS '18 MEMSYS |
| DOI: | 10.1145/3240302.3240322 |
| Popis: | Autonomous driving is disrupting conventional automotive development. Underlying reasons include control unit consolidation, the use of components originally developed for the consumer market, and the large amount of data that must be processed. For instance, Audi's zFAS or NVIDIA's Xavier platform integrate GPUs, custom accelerators, and CPUs within a single domain controller to perform sensor fusion, processing, and decision making. The communication between these heterogeneous components and the algorithms for Advanced Driver Assistance Systems and Autonomous Driving require low latency and huge memory bandwidth, bringing the Memory Wall from high-performance computing in data centers directly to our cars. In this paper we discuss these and other requirements in using DRAM for near-term autonomous driving architectures. |
| Databáze: | OpenAIRE |
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