A prototype of an adaptive computer vision algorithm on MPSoC architecture

Continuous software and hardware innovations impose on the one hand a high degree of flexibility from an algorithm and on the other hand it requires that a given processing architecture has the capability to adapt to changing computation patterns at run-time. In this work, we demonstrate how a computer vision application can adapt itself at runtime in order to satisfy different requirements of quality and throughput. For that, we consider an implementation of the Harris Corner Detector on an MPSoC (Multi-Processor System-on-Chip) architecture composed of a quad-core RISC processor and one accelerator based on a programmable massively parallel processor array.