A framework for optimizing OpenVX applications performance on embedded manycore accelerators

Abstract

Nowadays Computer Vision application are ubiquitous, and their presence on embedded devices is more and more widespread. Heterogeneous embedded systems featuring a clustered manycore accelerator are a very promising target to execute embedded vision algorithms, but the code optimization for these platforms is a challenging task. Moreover, designers really need support tools that are both fast and accurate. In this work we introduce ADRENALINE, an environment for development and optimization of OpenVX applications targeting manycore accelerators. ADRENALINE consists of a custom OpenVX run-time and a virtual platform, and overall it is intended to provide support to enhance performance of embedded vision applications.