  Optimising deep learning inference across edge devices and optimisation
targets such as inference time, memory footprint and power consumption is a key
challenge due to the ubiquity of neural networks. Today, production deep
learning frameworks provide useful abstractions to aid machine learning
engineers and systems researchers. However, in exchange they can suffer from
compatibility challenges (especially on constrained platforms), inaccessible
code complexity, or design choices that otherwise limit research from a systems
perspective. This paper presents Orpheus, a new deep learning framework for
easy prototyping, deployment and evaluation of inference optimisations. Orpheus
features a small codebase, minimal dependencies, and a simple process for
integrating other third party systems. We present some preliminary evaluation
results.
