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A Novel Integrated Simulation Framework for Cyber-Physical Systems Modelling

计算机科学 信息物理系统 嵌入式系统 分布式计算 模拟 计算机体系结构 操作系统
作者
Nikolaos Tampouratzis,Panagiotis Mousouliotis,Ioannis Papaefstathiou
出处
期刊:IEEE Transactions on Parallel and Distributed Systems [Institute of Electrical and Electronics Engineers]
卷期号:34 (10): 2684-2698 被引量:4
标识
DOI:10.1109/tpds.2023.3300081
摘要

The growing use of Cyber-Physical Systems (CPS) in a plethora of domains (e.g. healthcare, industry, smart homes, transportation, etc.) triggers an urgent demand for simulation frameworks that can simulate in an integrated manner all the components (i.e. CPUs, Memories, Networks, Physical Environment) of a system-under-design(SuD). By utilizing such a simulator, software design can proceed in parallel with physical development which results in the reduction of the so important time-to-market. The main problem, however, is that currently there is a shortage of such simulation frameworks; most simulators used for modelling the digital aspects of CPS applications (i.e. full-system CPU/Mem/Peripheral simulators) lack any support of the CPS physical aspects and vice versa. The presented fully-distributed simulation framework (APOLLON) is the first known open-source, high-performance simulator that can handle holistically complex CPSs including processors, peripherals, networks and physical aspects of them. APOLLON is an extension of the COSSIM simulation framework and it integrates, in a novel and efficient way, a combined processing and network simulator with the widely-used Ptolemy physical simulator, in a transparent way. Our highly integrated approach is further augmented with Machine Learning capabilities by implementing Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks in both the Cyber and Physical domains, enabling users to develop their complex recurrent neural networks significantly fast and accurately. APOLLON has been evaluated when executing a number of benchmarks and real-world use cases; the end results demonstrate that the presented approach has up to 99% accuracy in the reported SuD aspects.
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