Details
| Original language | English |
|---|---|
| Title of host publication | Big Data Analytics for Cyber-Physical Systems |
| Subtitle of host publication | Machine Learning for the Internet of Things |
| Editors | Guido Dartmann, Houbing Song, Anke Schmeink |
| Publisher | Elsevier |
| Chapter | 6 |
| Pages | 113-143 |
| Number of pages | 31 |
| ISBN (electronic) | 9780128166376 |
| ISBN (print) | 9780128166468 |
| Publication status | Published - 19 Jul 2019 |
Abstract
Complex driver-assistance systems that analyze driving situations based on a range of sensors enable autonomous driving vehicles-a key aspect of smart cities. This massive automation necessitates computationally powerful and energy-efficient hardware devices available in each individual driving unit. Heterogeneous multiprocessor system-on-chips provide excellent performance-to-power characteristics for the use in driver-assistance applications. Since these programmable chips use flexible software, they theoretically feature high maintainability and portability. However, due to the lack of programmability of different parallel and heterogeneous processing units, developers can barely fully exploit all computational capabilities. To overcome the gap between theoretical peak performance and the effectively gained speedup, diverse programming approaches and supportive tools have emerged. This work presents an overview of the most important trends and contributes a middleware approach for abstracting, and thus unifying, the programming for homogeneous and heterogeneous architectures.
Keywords
- Advanced driver-assistance systems, Architecture mapping, Design-space exploration, Embedded accelerators, Heterogeneous MPSoC, Multicore-software portability, Parallel programming
ASJC Scopus subject areas
- Social Sciences(all)
- General Social Sciences
Sustainable Development Goals
Cite this
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Big Data Analytics for Cyber-Physical Systems: Machine Learning for the Internet of Things. ed. / Guido Dartmann; Houbing Song; Anke Schmeink. Elsevier, 2019. p. 113-143.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research
}
TY - CHAP
T1 - Portable implementationsfor heterogeneous hardwareplatforms in autonomousdriving systems
AU - Arndt, Oliver Jakob
AU - Rallapalli, Parwesh
AU - Blume, Holger Christoph
PY - 2019/7/19
Y1 - 2019/7/19
N2 - Complex driver-assistance systems that analyze driving situations based on a range of sensors enable autonomous driving vehicles-a key aspect of smart cities. This massive automation necessitates computationally powerful and energy-efficient hardware devices available in each individual driving unit. Heterogeneous multiprocessor system-on-chips provide excellent performance-to-power characteristics for the use in driver-assistance applications. Since these programmable chips use flexible software, they theoretically feature high maintainability and portability. However, due to the lack of programmability of different parallel and heterogeneous processing units, developers can barely fully exploit all computational capabilities. To overcome the gap between theoretical peak performance and the effectively gained speedup, diverse programming approaches and supportive tools have emerged. This work presents an overview of the most important trends and contributes a middleware approach for abstracting, and thus unifying, the programming for homogeneous and heterogeneous architectures.
AB - Complex driver-assistance systems that analyze driving situations based on a range of sensors enable autonomous driving vehicles-a key aspect of smart cities. This massive automation necessitates computationally powerful and energy-efficient hardware devices available in each individual driving unit. Heterogeneous multiprocessor system-on-chips provide excellent performance-to-power characteristics for the use in driver-assistance applications. Since these programmable chips use flexible software, they theoretically feature high maintainability and portability. However, due to the lack of programmability of different parallel and heterogeneous processing units, developers can barely fully exploit all computational capabilities. To overcome the gap between theoretical peak performance and the effectively gained speedup, diverse programming approaches and supportive tools have emerged. This work presents an overview of the most important trends and contributes a middleware approach for abstracting, and thus unifying, the programming for homogeneous and heterogeneous architectures.
KW - Advanced driver-assistance systems
KW - Architecture mapping
KW - Design-space exploration
KW - Embedded accelerators
KW - Heterogeneous MPSoC
KW - Multicore-software portability
KW - Parallel programming
UR - http://www.scopus.com/inward/record.url?scp=85081302733&partnerID=8YFLogxK
U2 - 10.1016/B978-0-12-816637-6.00006-3
DO - 10.1016/B978-0-12-816637-6.00006-3
M3 - Contribution to book/anthology
SN - 9780128166468
SP - 113
EP - 143
BT - Big Data Analytics for Cyber-Physical Systems
A2 - Dartmann, Guido
A2 - Song, Houbing
A2 - Schmeink, Anke
PB - Elsevier
ER -