  Deep neural networks (DNNs) are widely used in autonomous driving due to
their high accuracy for perception, decision, and control. In safety-critical
systems like autonomous driving, executing tasks like sensing and perception in
real-time is vital to the vehicle's safety, which requires the application's
execution time to be predictable. However, non-negligible time variations are
observed in DNN inference. Current DNN inference studies either ignore the time
variation issue or rely on the scheduler to handle it. None of the current work
explains the root causes of DNN inference time variations. Understanding the
time variations of the DNN inference becomes a fundamental challenge in
real-time scheduling for autonomous driving. In this work, we analyze the time
variation in DNN inference in fine granularity from six perspectives: data,
I/O, model, runtime, hardware, and end-to-end perception system. Six insights
are derived in understanding the time variations for DNN inference.
