End-to-End Automation Frameworks for Mapping Neural Networks onto Embedded Devices and Early Performance Predictions: A Survey

Abstract

Recently automated frameworks have been proposed, mapping neural networks from a high-level description onto embedded devices, most of them in an end-to-end manner. This paper aims to give an overview of their main characteristics and achievements. A special focus is lying on internal predictions during design space exploration (DSE) regarding hardware targets (performance, area or power consumption), enabling fast exploration of the individually defined search spaces, especially in early design stages. Additionally, recent research results that are not part of such frameworks, but present novel estimation techniques are also covered by this work.