A reconfigurable computing platform for real time embedded applications

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2011

Öğrenci: FATİH SAY

Danışman: CÜNEYT FEHMİ BAZLAMAÇCI

Özet:

Today’s reconfigurable devices successfully combine ‘reconfigurable computing machine’ paradigm and ‘high degree of parallelism’ and hence reconfigurable computing emerged as a promising alternative for computing-intensive applications. Despite its superior performance and lower power consumption compared to general purpose computing using microprocessors, reconfigurable computing comes with a cost of design complexity. This thesis aims to reduce this complexity by providing a flexible and user friendly development environment to application programmers in the form of a complete reconfigurable computing platform. The proposed computing platform is specially designed for real time embedded applications and supports true multitasking by using available run time partially reconfigurable architectures. For this computing platform, we propose a novel hardware task model aiming to minimize logic resource requirement and the overhead due to the reconfiguration of the device. Based on this task model an optimal 2D surface partitioning strategy for managing the hardware resource is presented. A mesh network-on-chip is designed to be used as the communication environment for the hardware tasks and a runtime mapping technique is employed to lower the communication overhead. As the requirements of embedded systems are known prior to field operation, an o ine design flow is proposed for generating the associated bit-stream for the hardware tasks. Finally, an online real time operating system scheduler is given to complete the necessary building blocks of a reconfigurable computing platform suitable for real time computing-intensive embedded applications. In addition to providing a flexible development environment, the proposed computing platform is shown to have better device utilization and reconfiguration time overhead compared to existing studies.