0000000000585800
AUTHOR
Marco Platzner
Enabling XCSF to cope with dynamic environments via an adaptive error threshold
The learning classifier system XCSF is a variant of XCS employed for function approximation. Although XCSF is a promising candidate for deployment in autonomous systems, its parameter dependability imposes a significant hurdle, as a-priori parameter optimization is not feasible for complex and changing environmental conditions. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions that change during runtime, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved …
Evolution of application-specific cache mappings
Reconfigurable caches offer an intriguing opportunity to tailor cache behavior to applications for better run-times and energy consumptions. While one may adapt structural cache parameters such as cache and block sizes, we adapt the memory-address-to-cache-index mapping function to the needs of an application. Using a LEON3 embedded multi-core processor with reconfigurable cache mappings, a metaheuristic search procedure, and MiBench applications, we show in this work how to accurately compare non-deterministic performances of applications and how to use this information to implement an optimization procedure that evolves application-specific cache mappings for the LEON3 multi-core processo…
Optimal and Greedy Heuristic Approaches for Scheduling and Mapping of Hardware Tasks to Reconfigurable Computing Devices
Executing real-time tasks on dynamically reconfigurable FPGAs requires us to solve the challenges of scheduling and placement. In the past, many approaches have been presented to address these challenges. Still, most of them rely on idealized assumptions about the reconfigurability of FPGAs and the capabilities of commercial tool flows. In our work, we aim at solving these problems leveraging a practically useful 2D slot-based FPGA area model. We present optimal approaches for reconfigurable slot creation, hardware task assignment, and placement creation. We quantitatively compare optimal and heuristics algorithms through simulation experiments and show that the heuristics are rather close …
Optimization of Application-Specific L1 Cache Translation Functions of the LEON3 Processor
Reconfigurable caches offer an intriguing opportunity to tailor cache behavior to applications for better run-times and energy consumptions. While one may adapt structural cache parameters such as cache and block sizes, we adapt the memory-address-to-cache-index mapping function to the needs of an application. Using a LEON3 embedded multi-core processor with reconfigurable cache mappings, a metaheuristic search procedure, and Mibench applications, we show in this work how to accurately compare non-deterministic performances of applications and how to use this information to implement an optimization procedure that evolves application-specific cache mappings.
An adaption mechanism for the error threshold of XCSF
Learning Classifier System (LCS) is a class of rule-based learning algorithms, which combine reinforcement learning (RL) and genetic algorithm (GA) techniques to evolve a population of classifiers. The most prominent example is XCS, for which many variants have been proposed in the past, including XCSF for function approximation. Although XCSF is a promising candidate for supporting autonomy in computing systems, it still must undergo parameter optimization prior to deployment. However, in case the later deployment environment is unknown, a-priori parameter optimization is not possible, raising the need for XCSF to automatically determine suitable parameter values at run-time. One of the mo…