Hardware Cost and Energy Consumption Optimization for Safety-Critical Applications on Heterogeneous Distributed Embedded Systems

Zou, Wenchao; Li, Renfa; Wu, Wufei; Zeng, Lining

doi:10.1109/padsw.2018.8644635

Cited by 3 publications

(1 citation statement)

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“…In [18], the authors present two heuristic algorithms to minimize hardware cost for parallel embedded systems, where both the functional safety and real-time requirements are considered. In [19], the authors combine genetic algorithm and simulated annealing to reduce the hardware cost and energy consumption of embedded products while satisfying the hard real-time and reliability requirements of safety-critical applications. In [20], the authors present three price performance-driven heuristic algorithms for hardware cost minimization of embedded systems, which consider both real-time and reliability requirement.…”

Section: Related Workmentioning

confidence: 99%

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Xie

Guo

Yang

et al. 2021

Sensors

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The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.

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Section: Related Workmentioning

confidence: 99%