Edge AI: Framework for Healthcare Applications
Deploying AI/ML models on Edge Devices
(Darwin Edge, Switzerland) Debmalya Biswas, Miljan Vuletić, Vladimir Mujagić, Nikola Milojević
Abstract. Edge AI enables intelligent solutions to be deployed on edge devices, reducing latency, allowing offline execution, and providing strong privacy guarantees. Unfortunately, achieving efficient and accurate execution of AI algorithms on edge devices, with limited power and computational resources, raises several deployment challenges. Existing solutions are very specific to a hardware platform/vendor. In this work, we present the MATE framework that provides tools to (1) foster model-to-platform adaptations, (2) enable validation of the deployed models proving their alignment with the originals, and (3) empower engineers and architects to do it efficiently using repeated, but rapid development cycles. We finally show the practical utility of the proposal by applying it on a real-life healthcare body-pose estimation app.
This paper has been accepted for presentation at the 4th IJCAI Workshop on AI for Ageing, Rehabilitation and Intelligent Assisted Living (ARIAL), Montreal, Aug 2021 (pdf) (ppt)
