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The acceleration of Computer Vision algorithms is an important enabler to support the more and more pervasive applications of the embedded vision domain. Heterogeneous systems featuring a clustered many-core accelerator are a very promising target for embedded vision workloads, but the code optimization for these platforms is a challenging task. In this work we introduce ADRENALINE 1, a novel framework for fast prototyping and optimization of OpenVX applications for heterogeneous SoCs with many-core accelerators. ADRENALINE consists of an optimized OpenVX run-time system and a virtual platform, and it is intended to provide support to a wide range of end users. We highlight the benefits of this approach in different optimization contexts.
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Tagliavini et al. (2015) studied this question.
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