Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

Using Learning Classifier Systems for the DSE of Dynamically Adaptable Embedded Systems

Smirnov F, Pourmohseni B, Teich J (2020)

---

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2020

Event location: Grenoble

Abstract

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 increasing dynamism of embedded systems has led to the emergence of novel design 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 purely static design approach, which statically designs a single design solution which compromises between the possible run-time situations, the off-line DSE of these so-called hybrid approaches provides 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,
the modeling of complex dynamic environments or run-time requirements is either not possible or comes at the cost of significant computation overheads or results of lower quality. As a remedy, this paper introduces Learning Optimizer Constrained by ALtering conditions (LOCAL), a novel optimization framework for the optimization of dynamically adaptable 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. The framework enables the designer to model complex environmental behavior, making this problem-specific knowledge accessible to the optimizer. We show how the proposed framework—which can be used for the optimization of any dynamic 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.

Authors with CRIS profile

How to cite

APA:

Smirnov, F., Pourmohseni, B., & Teich, J. (2020). Using Learning Classifier Systems for the DSE of Dynamically Adaptable Embedded Systems. In Proceedings of the Design, Automation and Test in Europe Conference. Grenoble, FR.

MLA:

Smirnov, Fedor, Behnaz Pourmohseni, and Jürgen Teich. "Using Learning Classifier Systems for the DSE of Dynamically Adaptable Embedded Systems." Proceedings of the Design, Automation and Test in Europe Conference, Grenoble 2020.

BibTeX: Download