The increasing computing demands of autonomous driving applications have driven the adoption of multicore processors in real-time systems, which in turn renders energy optimizations critical for reducing battery capacity and vehicle weight. A typical energy optimization method targeting traditional real-time systems finds a critical speed under a static deadline, resulting in conservative energy savings that are unable to exploit dynamic changes in the system and environment. We capture emerging dynamic deadlines arising from the vehicle’s change in velocity and driving context for an additional energy optimization opportunity. In this article, we extend the preliminary work for uniprocessors [
- Award ID(s):
- 1704859
- NSF-PAR ID:
- 10486666
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Embedded Computing Systems
- Volume:
- 22
- Issue:
- 3
- ISSN:
- 1539-9087
- Page Range / eLocation ID:
- 1 to 29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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