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Methodology and Tools for Energy-aware Task Mapping on Heterogeneous Multiprocessor Architectures

Abstract : During the last decade, the design of embedded systems was pushed to increase computational power while maintaining low energy consumption. As an example, autonomous vehicles such as drones are a representative application domain which combines vision, wireless communications and other computation intensive kernels constrained with a limited energy budget. With the advent of Multiprocessor System-on-Chip (MpSoC) architectures, simplification of processor cores decreased power consumption per operation, while the multiplication of cores brought performance improvement. However, the dark silicon issue led to the benefit of augmenting programmable processors with specialized hardware accelerators and to the rise of Heterogeneous MpSoC (HMpSoC) combining both software (SW) and hardware (HW) computational resources. For these heterogeneous architectures, performance and energy consumption depend on a large set of parameters such as the HW/SW partitioning, the type of HW implementation or the communication cost between HW and SW cores therefore leading to a huge design space. In this thesis, we study how to reduce the development and implementation complexity of energy-efficient applications on HMpSoC. Multiple contributions are proposed to enhance Design Space Exploration (DSE) tools with energy objectives. First, a formal definition of HMpSoC structure is introduced alongside with a generic representation focused on the memory hierarchy. Then, a fast power modelling tool is proposed and validated on several applications. This power model separates the power sources in three families (static, dynamic computation and dynamic communication) and computes their contributions on global consumption independently. With a fine grain communications study, this approach rapidly computes energy consumption for a given application mapping on a HMpSoC. In a second time, we propose a methodology for energy-driven accelerator exploration on HMpSoC. This method builds upon the previous power model coupled with an Mixed Integer Linear Programming (MILP) formulation and enables to efficiently select HW accelerators and HW/SW partitioning which achieve energy efficient-mapping of a tiled application. The experiments involved in these contributions show the complexity of DSE validation process on a wide range of applications and architectures. To address these issues, we introduce a HMpSoC simulator embedding a power model to monitor application execution. Properties of targeted architectures are described, at run-time with the previous generic representation model. Furthermore, this simulator is coupled with an application generator framework that could build an infinite set of representative applications following predefined computation models. The obtained applications could then be enriched with mapping directive and executed on the simulator. This combination enables to ease the research and validation of new DSE algorithms targeting energy-aware application mapping on a wide range of HMpSoC architectures.
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Submitted on : Wednesday, December 27, 2017 - 12:33:52 PM
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  • HAL Id : tel-01672814, version 1


Baptiste Roux. Methodology and Tools for Energy-aware Task Mapping on Heterogeneous Multiprocessor Architectures. Embedded Systems. Université de Rennes 1, 2017. English. ⟨tel-01672814⟩



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