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Optimising deep learning inference across edge devices and optimisationtargets such as inference time, memory footprint and power consumption is a keychallenge due to the ubiquity of neural networks. Today, production deeplearning frameworks provide useful abstractions to aid machine learningengineers and systems researchers. However, in exchange they can suffer fromcompatibility challenges (especially on constrained platforms), inaccessiblecode complexity, or design choices that otherwise limit research from a systemsperspective. This paper presents Orpheus, a new deep learning framework foreasy prototyping, deployment and evaluation of inference optimisations. Orpheusfeatures a small codebase, minimal dependencies, and a simple process forintegrating other third party systems. We present some preliminary evaluationresults.

Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference