Mistify: Automating DNN Model Porting for On-Device Inference at the Edge

AI applications powered by deep learning inference are increasingly run natively on edge devices to provide better interactive user experience. This often necessitates fitting a model originally designed and trained in the cloud to edge devices with a range of hardware capabilities, which so far has relied on time-consuming manual effort.

In this paper, we quantify the challenges of manually generating a large number of compressed models and then build a system framework, Mistify, to automatically port a cloudbased model to a suite of models for edge devices targeting various points in the design space. Mistify adds an intermediate "layer" that decouples the model design and deployment phases. By exposing configuration APIs to obviate the need for code changes deeply embedded into the original model, Mistify hides run-time issues from model designers and hides the model internals from model users, hence reducing the expertise needed in either. For better scalability, Mistify consolidates multiple model tailoring requests to minimize repeated computation. Further, Mistify leverages locally available edge data in a privacy-aware manner, and performs run-time model adaptation to provide scalable edge support and accurate inference results. Extensive evaluation shows that Mistify reduces the DNN porting time needed by over 10⇥ to cater to a wide spectrum of edge deployment scenarios, incurring orders of magnitude less manual effort.