Energy and Design Cost Efficiency for Streaming Applications on Systems-on-Chip
Abstract: With the increasing capacity of today's integrated circuits, a number ofheterogeneous system-on-chip (SoC) architectures in embedded systemshave been proposed. In order to achieve energy and design cost efficientstreaming applications on these systems, new design space explorationframeworks and performance analysis approaches are required. Thisthesis considers three state-of-the-art SoCs architectures, i.e., themulti-processor SoCs (MPSoCs) with network-on-chip (NoC) communication,the hybrid CPU/FPGA architectures, and the run-time reconfigurable (RTR)FPGAs. The main topic of the author?s research is to model and capturethe application scheduling, architecture customization, and bufferdimensioning problems, according to the real-time requirement. Sincethese problems are NP-complete, heuristic algorithms and constraintprogramming solver are used to compute a solution.For NoC communication based MPSoCs, an approach to optimize thereal-time streaming applications with customized processorvoltage-frequency levels and memory sizes is presented. A multi-clockedsynchronous model of computation (MoC) framework is proposed inheterogeneous timing analysis and energy estimation. Using heuristicsearching (i.e., greedy and taboo search), the experiments show anenergy reduction (up to 21%) without any loss in application throughputcompared with an ad-hoc approach.On hybrid CPU/FPGA architectures, the buffer minimization scheduling ofreal-time streaming applications is addressed. Based on event models,the problem has been formalized decoratively as constraint basescheduling, and solved by public domain constraint solver Gecode.Compared with traditional PAPS method, the proposed method needssignificantly smaller buffers (2.4% of PAPS in the best case), whilehigh throughput guarantees can still be achieved.Furthermore, a novel compile-time analysis approach based on iterativetiming phases is proposed for run-time reconfigurations in adaptivereal-time streaming applications on RTR FPGAs. Finally, thereconfigurations analysis and design trade-offs analysis capabilities ofthe proposed framework have been exemplified with experiments on bothexample and industrial applications.
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