  As the usage of deep learning becomes increasingly popular in mobile and
embedded solutions, it is necessary to convert the framework-specific network
representations into executable code for these embedded platforms. This paper
consists of two parts: The first section is made up of a survey and benchmark
of the available open source deep learning compiler toolchains, which focus on
the capabilities and performance of the individual solutions in regard to
targeting embedded devices and microcontrollers that are combined with a
dedicated accelerator in a heterogeneous fashion. The second part explores the
implementation and evaluation of a compilation flow for such a heterogeneous
device and reuses one of the existing toolchains to demonstrate the necessary
steps for hardware developers that plan to build a software flow for their own
hardware.
