  Radical progress in the field of deep learning (DL) has led to unprecedented
accuracy in diverse inference tasks. As such, deploying DL models across mobile
platforms is vital to enable the development and broad availability of the
next-generation intelligent apps. Nevertheless, the wide and optimised
deployment of DL models is currently hindered by the vast system heterogeneity
of mobile devices, the varying computational cost of different DL models and
the variability of performance needs across DL applications. This paper
proposes OODIn, a framework for the optimised deployment of DL apps across
heterogeneous mobile devices. OODIn comprises a novel DL-specific software
architecture together with an analytical framework for modelling DL
applications that: (1) counteract the variability in device resources and DL
models by means of a highly parametrised multi-layer design; and (2) perform a
principled optimisation of both model- and system-level parameters through a
multi-objective formulation, designed for DL inference apps, in order to adapt
the deployment to the user-specified performance requirements and device
capabilities. Quantitative evaluation shows that the proposed framework
consistently outperforms status-quo designs across heterogeneous devices and
delivers up to 4.3x and 3.5x performance gain over highly optimised platform-
and model-aware designs respectively, while effectively adapting execution to
dynamic changes in resource availability.
