M3: Towards Efficient Mixed Machine Learning Model Co-Location on Constrained Edge Devices

This paper explores the effects of machine learning (ML) model co-location on resource-constrained edge devices, and proposes M3, a mixed machine learning model co-location framework that leverages techniques such as network architecture search to generate resource-aware models, as well as quantization to further reduce resource requirements, alongside a runtime that dynamically switches between resource-aware models to serve specific tasks.