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Reports (Research Report) Year : 2017

Models of Architecture: Application to ESL Model-Based Energy Consumption Estimation


Current trends in high performance and embedded computing include design of increasingly complex hardware architectures with high parallelism, heterogeneous processing elements and non-uniform communication resources. In order to take hardware and software design decisions, early evaluations of the system non-functional properties are needed. These evaluations of system efficiency require Electronic System-Level (ESL) information on both the algorithms and the architecture. Contrary to algorithm models for which a major body of work has been conducted on defining formal Models of Computation (MoCs), architecture models from the literature are mostly empirical models from which reproducible experimentation requires the accompanying software. In this report, a precise definition of a Model of Architecture (MoA) is proposed that focuses on reproducibility and abstraction and removes the overlap previously existing between the notions of MoA and MoC. A first MoA, called the Linear System-Level Architecture Model (LSLA), is presented. To demonstrate the generic nature of the proposed new architecture modeling concepts, we show that the LSLA Model can be integrated flexibly with different MoCs. LSLA is then used to model the energy consumption of a State-of-the-Art Multiprocessor System-on-Chip (MPSoC) when running an application described using the Synchronous Dataflow (SDF) MoC. A method to automatically learn LSLA model parameters from platform measurements is introduced. Despite the high complexity of the underlying hardware and software, a simple LSLA model is demonstrated to estimate the energy consumption of the MPSoC with a fidelity of 86%.
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hal-01464856 , version 1 (10-02-2017)


  • HAL Id : hal-01464856 , version 1


Maxime Pelcat, Alexandre Mercat, Karol Desnos, Luca Maggiani, Yanzhou Liu, et al.. Models of Architecture: Application to ESL Model-Based Energy Consumption Estimation. [Research Report] IETR/INSA Rennes; Scuola Superiore Sant’Anna, Pisa; Institut Pascal; University of Maryland, College Park; Tampere University of Technology, Tampere. 2017. ⟨hal-01464856⟩
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