Memory management in embedded vision systems: Optimization problems and solution methods

Abstract

Embedded vision systems design faces a memory-wall kind of challenge: images are big, and therefore memories containing them have high latency; and still, high performance is desired. For the case of non-linear processings, Mancini and Rousseau (Proc. DATE 2012) have designed a software generator of adhoc memory hierarchies, called Memory Management Optimization (MMOpt). While the performance of the generated circuits is very good, design-time decisions have to be made regarding their operation in order to handle finely the compromise between the usual metrics of design area, energy consumption, and performance. This study tackles the optimization challenge set by the design of the operational behavior of the memory hierarchy generated by MMOpt. After a precise formulation as a 3-objective optimization problem is given, two algorithms are proposed, and their performance is analyzed on real-world processings against the previously proposed algorithms. The results show a reduction of the amount of transferred data by 17% on average, and of the computing times by 11.7%, for the same design area.