  The state-of-the-art driving automation system demands extreme computational
resources to meet rigorous accuracy and latency requirements. Though emerging
driving automation computing platforms are based on ASIC to provide better
performance and power guarantee, building such an accelerator-based computing
platform for driving automation still present challenges. First, the workloads
mix and performance requirements exposed to driving automation system present
significant variability. Second, with more cameras/sensors integrated in a
future fully autonomous driving vehicle, a heterogeneous multi-accelerator
architecture substrate is needed that requires a design space exploration for a
new form of parallelism. In this work, we aim to extensively explore the above
system design challenges and these challenges motivate us to propose a
comprehensive framework that synergistically handles the heterogeneous hardware
accelerator design principles, system design criteria, and task scheduling
mechanism. Specifically, we propose a novel heterogeneous multi-core AI
accelerator (HMAI) to provide the hardware substrate for the driving automation
tasks with variability. We also define system design criteria to better utilize
hardware resources and achieve increased throughput while satisfying the
performance and energy restrictions. Finally, we propose a deep reinforcement
learning (RL)-based task scheduling mechanism FlexAI, to resolve task mapping
issue. Experimental results show that with FlexAI scheduling, basically 100%
tasks in each driving route can be processed by HMAI within their required
period to ensure safety, and FlexAI can also maximally reduce the breaking
distance up to 96% as compared to typical heuristics and guided
random-search-based algorithms.
