Compiler Toolchains for Deep Learning Workloads on Embedded Platforms

Max Sponner, Bernd Waschneck, Akash Kumar

Keywords: deep learning, embedded, deep learning compiler, embedded deep learning

TL;DR: A survey of available deep learning compilers as well as an example implementation for a new accelerator device.

Abstract:

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 starts with a survey and benchmark of the available open source deep learning compiler toolchains, which focuses on the capabilities and performance of the toolchains in regard to targeting embedded microcontrollers that are combined with a dedicated accelerator in a heterogeneous fashion.
The second part focuses on the implementation and evaluation of a compilation flow that targets such a solution and reuses one of the existing toolchains to demonstrate the necessary steps for hardware developers to build a software flow for their product.