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- W2798434269 abstract "Embedded control applications are widely implemented on small, low-cost and resource-constrained microcontrollers, e.g., in the automotive domain. Conventionally, control algorithms are designed using model-based approaches, without considering the details of the implementation platform. This leads to inefficient utilization of the resources. With the emergence of the cyber-physical system (CPS)-oriented thinking, there has lately been a strong interest in co-design of control algorithms and their implementation platforms. Some recent efforts have shown that a schedule on multiple applications with more on-chip cache reuse is able to improve the control performance. However, it has not been studied how the control performance can be maximized for a given schedule and how an optimal schedule can be computed. In this work, we propose a two-stage framework to compute the schedule maximizing the overall control performance of all the applications. First, a holistic controller design taking all the sampling periods and sensing-to-actuation delays in a schedule into account is presented, aiming to maximize the overall control performance. Second, a hybrid search algorithm for discrete decision space is reported to efficiently compute an optimal schedule. Experimental results on a case study with multiple automotive applications show that a significant improvement of 10–20% in control performance can be achieved by the proposed cache-aware scheduling approach." @default.
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- W2798434269 date "2018-03-01" @default.
- W2798434269 modified "2023-09-22" @default.
- W2798434269 title "Cache-aware task scheduling for maximizing control performance" @default.
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- W2798434269 doi "https://doi.org/10.23919/date.2018.8342098" @default.
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