Efficient Scheduling, Mapping and Resource Prediction for Dynamic Run time Operating Systems

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Date

2015-05-08

Authors

Al-Wattar, Ahmed

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Volume Title

Publisher

University of Guelph

Abstract

Efficient Scheduling, Mapping and Resource Prediction for Dynamic Run time Operating Systems Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing and network processors. Time multiplexing of reconfigurable hardware resources raises a number of new issues, ranging from run-time systems to complex programming models that usually form a Reconfigurable hardware Operating System (ROS). In this thesis a novel ROS framework that aids the designer from the early design stages all the way down to the hardware implementation is proposed. An efficient reconfigurable platform was implemented along with several novel scheduling algorithms. The algorithms proposed tend to reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software/hardware to efficiently utilize resources and reduce computation time. A framework for efficient mapping of execution units to task graphs in a runtime reconfigurable system is also designed. The framework utilizes an Island Based Genetic Algorithm flow that optimizes several objectives including performance, area and power consumption. The proposed Island based GA framework achieves on average 55.2% improvement over a single GA implementation and 80.7% improvement over a baseline random allocation and binding approach. Finally, we present a novel adaptive and dynamic methodology based on a Machine Learning approach for predicting and estimating the necessary resources for an application based on past historical information. An important feature of the proposed methodology is that the system is able to learn and generalize and, therefore, is expected to improve its accuracy over time.

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Keywords

Reconfigurable Computing, Operating systems, Task migration, FPGA, Reconfigurable Scheduling, Reconfigurable Scheduling, Reconfigurable Operating systems, Island Based Genetic algorithm, Genetic algorithm, Task Reuse, Hardware tasks, Machine learning, Multiple Execution Variants, Classification, Partial reconfiguration, Predicting hardware resources, Dynamic run-time reconfigurtion, Optimization, Design Exploration, Resource Estimation, Scheduling Algorithms, Embedded Systems

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