Most robotic vision algorithms are computationally intensive and operate on millions of pixels of real-time video sequences. But they offer a high degree of parallelism that can be exploited through parallel computing techniques like Invasive Computing. But the conventional way of multi-processing alone (with static resource allocation) is not sufficient enough to handle a scenario like robotic maneuver, where processing elements have to be shared between various applications and the computing requirements of such applications may not be known entirely at compile-time. Such static mapping schemes leads to inefficient utilization of resources. At the same time it is difficult to dynamically control and distribute resources among different applications running on a single chip, achieving high resource utilization under high-performance constraints. Invasive Computing obtains more importance under such circumstances, where it offers resource awareness to the application programs so that they can adapt themselves to the changing conditions, at run-time. In this paper we demonstrate the resource aware and self-organizing behavior of invasive applications using three widely used applications from the area of robotic vision - Optical Flow, Object Recognition and Disparity Map Computation. The applications can dynamically acquire and release hardware resources, considering the level of parallelism available in the algorithm and time-varying load.
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