Efficient algorithms for memory management in embedded vision systems

In the field of embedded vision systems, meeting the constraints on design criteria such as performance, area, and power consumption can be a real challenge. In fact, to alleviate the well known “Memory Mall”, it is mandatory to provide efficient memory hierarchies to reach usable performance for the system to be designed when it has to handle non-linear image treatments. To address this problematic, Mancini and Rousseau (Proc.DATE, 2012) have designed a software generator of memory hierarchies for each non-linear image operation. It allows one to improve dramatically the performance of the system, while moderately increasing its area and energy consumption. The trade-offs between these three parameters are then taken to the level of the design of the operation of this memory hierarchy, a problem that can be formalized as a 3-objective optimization problem. In this study, we formalize this problem and give new approaches both for the problem and particular sub-problems. The results on the same real-world data set as used by Mancini and Rousseau (Proc.DATE, 2012) show a very significant improvement and reduce the amount of transferred data up to 30% and a reduction of the computing time up to 15%.