Resource Optimization for Real-Time Streaming Applications Using Task Replication

Niknam, Sobhan; Wang, Peng; Stefanov, Todor

doi:10.1109/tcad.2018.2857039

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…In [21], the authors present a multi-population genetic algorithm towards optimizing both operation time and the number of required processing units for distributed real-time systems. In [22], the authors try to reduce the hardware cost by minimize the number of required processors to schedule an application, where considerably memory requirements and application latency are reduced by comparing with related approaches while meeting the same throughput constraint. In [23], the authors shows how to minimize the number of required processors for feasible running the parallel real-time tasks.…”

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%