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Driving into the memory wall

The role of memory for advanced driver assistance systems and autonomous driving
: Jung, Matthias; McKee, Sally A.; Sudarshan, Chirag; Dropmann, Christoph; Weis, Christian; Wehn, Norbert


Jacob, B. ; Association for Computing Machinery -ACM-:
International Symposium on Memory Systems, MEMSYS 2018. Proceedings : Alexandria, Virginia, October 01 - 04, 2018
New York: ACM, 2018
ISBN: 978-1-4503-6475-1
International Symposium on Memory Systems (MEMSYS) <2018, Alexandria/Va.>
Deutsche Forschungsgemeinschaft DFG
WE2442/10-1; 3D-DRAM
European Commission EC
H2020; 732631; OPRECOMP
Open transPREcision COMPuting
Fraunhofer IESE ()
ADAS; autonomous driving; DRAM

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.