  Advanced Driver-Assistance Systems (ADAS) is one of the primary drivers
behind increasing levels of autonomy, driving comfort in this age of connected
mobility. However, the performance of such systems is a function of execution
rate which demands on-board platform-level support. With GPGPU platforms making
their way into automobiles, there exists an opportunity to adaptively support
high execution rates for ADAS tasks by exploiting architectural heterogeneity,
keeping in mind thermal reliability and long-term platform aging. We propose a
future-proof, learning-based adaptive scheduling framework that leverages
Reinforcement Learning to discover suitable scenario based task-mapping
decisions for accommodating increased task-level throughput requirements.
