An End-to-End Embedded Neural Architecture Search and Model Compression Framework for Healthcare Applications and Use-Cases

Abstract

Deep learning has had a major impact in a wide range of research domains across the world, including healthcare and medicine. From aiding radiologists, by acting as clinical assistants, to analyzing electronic health records, deep learning models have proved to be beneficial in identifying health abnormalities and aiding diagnostics. This chapter discusses a framework that can be used to explore the design space of embedded neural network models for healthcare applications and use-cases, given the user quality requirements, such as accuracy or precision, and hardware constraints of the target execution platform. The models explored by the framework are successful in reducing the hardware overhead of network by a factor of 53 × while achieving a quality loss of <0.2% compared to state of the art.