Using Learning Classifier Systems for the DSE of Adaptive Embedded Systems

Modern embedded systems are not only becoming more and more complex but are also often exposed to dynamically changing run-time conditions such as resource availability or processing power requirements. This trend has led to the emergence of adaptive systems which are designed using novel approaches that combine a static off-line Design Space Exploration (DSE) with the consideration of the dynamic run-time behavior of the system under design. In contrast to a static design approach, which provides a single design solution as a compromise between the possible run-time situations, the off-line DSE of these so-called hybrid design approaches yields a set of configuration alternatives, so that at run time, it becomes possible to dynamically choose the option most suited for the current situation. However, most of these approaches still use optimizers which were primarily developed for static design. Consequently, modeling complex dynamic environments or run-time requirements is either not possible or comes at the cost of a significant computation overhead or results of poor quality. As a remedy, this paper introduces Learning Optimizer Constrained by ALtering conditions (LOCAL), a novel optimization framework for the DSE of adaptive embedded systems. Following the structure of Learning Classifier System (LCS) optimizers, the proposed framework optimizes a strategy, i.e., a set of conditionally applicable solutions for the problem at hand, instead of a set of independent solutions. We show how the proposed framework-which can be used for the optimization of any adaptive system-is used for the optimization of dynamically reconfigurable many-core systems and provide experimental evidence that the hereby obtained strategy offers superior embeddability compared to the solutions provided by a s.o.t.a. hybrid approach which uses an evolutionary algorithm.