  The widespread use of Deep Learning (DL) applications in science and industry
has created a large demand for efficient inference systems. This has resulted
in a rapid increase of available Hardware Accelerators (HWAs) making comparison
challenging and laborious. To address this, several DL hardware benchmarks have
been proposed aiming at a comprehensive comparison for many models, tasks, and
hardware platforms. Here, we present our DL hardware benchmark which has been
specifically developed for inference on embedded HWAs and tasks required for
autonomous driving. In addition to previous benchmarks, we propose a new
granularity level to evaluate common submodules of DL models, a twofold
benchmark procedure that accounts for hardware and model optimizations done by
HWA manufacturers, and an extended set of performance indicators that can help
to identify a mismatch between a HWA and the DL models used in our benchmark.
