DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms

Mohammadi, Hassan Ghasemzadeh; Arshad, Rahil; Rautmare, Sneha; Manjunatha, S; Kuschel, Maurice; Jentzsch, Felix; Platzner, Marco; Boschmann, Alexander; Schollbach, Dirk

doi:10.1109/etfa46521.2020.9211880

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…To meet such stringent requirements, large hardware manufacturers such as Intel and AMD (Xilinx) started to produce integrated systems characterized by heterogeneous components: the MPSoC [5]. As evidenced by [6] [7] [8] [9], many industries had already chosen to use MPSoCs as edge nodes to deploy multiple specialized tasks onto a single board to lower space, weight, power, and cost (SWaP-C) of their services. These SoCs are characterized by heterogeneous hardware components such as APUs for general-purpose computation, FPGAs (Field Programmable Gate Array) for high-performance computing (HPC), RPUs for real-time and safety computation, and GPUs (Graphic Processing Units) for parallel and graphical computations.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: RPUGuard: a Building Block for Next-Generation MPSoCs Hypervisors toward Mixed-Criticality Systems

Ottaviano

Cinque

Tommasi

et al. 2023

Preprint

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The spread of cutting-edge virtualization methods for modern embedded architectures like MultiProcessor Systems on Chip (MPSoCs) opens the door to the development of powerful and dependable hypervisors which might help developers to deal with complexity and heterogeneity of modern technologies while maintaining real-time requirements for critical applications. Following the needs of industrial applications, virtualization has been proven to be among the best approaches for the realization of mixed criticality systems embracing the fog computing paradigm while lowering the space, weight, power and cost (SWaP-C) of the deployment. However, the virtualization support for important hardware accelerators presents on MPSoCs, such as Real-Time Processing Units (RPUs), used for real-time and/or safety-critical workloads, is still overlooked. In this paper we propose the concept of the Omnivisor a software layer that virtualize an entire MPSoC improving the resource utilization while simplifying the implementation of a mixed criticality system over these heterogeneous boards. We identify the RPU virtualization as a building block for its realization. Therefore, as a major contribution for this paper, we design and implement a component, named RPUGuard, which is able to guarantee isolated communication channels with a fixed bandwidth between virtual machines, running on regular Application Processing Units (APUs), and the RPU on the same MPSoC. We evaluated RPUGuard on the Zynq Ultrascale+ board, in the context of a challenging case study concerning the magnetic control system of the ITER experimental nuclear fusion reactor. Results demonstrate how our solution can mitigate the weaknesses of current asymmetric communication techniques, while providing isolation guarantees to critical communication channels.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: RPUGuard: a Building Block for Next-Generation MPSoCs Hypervisors toward Mixed-Criticality Systems

Ottaviano

Cinque

Tommasi

et al. 2023

Preprint

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“…For example, this scenario naturally fits the potential of FPGA-based MPSoC platforms, where compute-intensive tasks are accelerated through hardware-implemented blocks (e.g. the different layers of multi-channel convolutional neural networks, used as a deep learning framework for condition monitoring and fault detection [3], as suggested in Figure 3), while less demanding tasks like sensor data preprocessing are performed in software by the virtualized CPUs made available to the acceleration VM. Last, a noncritical general-purpose VM can simultaneously manage other functions, e.g.…”

Section: Use Case Scenariomentioning

confidence: 99%

“…As a specific example of a mixed-criticality workload at the edge level, let us consider a wind turbine plant, needing real-time control as well as monitoring of significant amounts of electrical and mechanical parameters for predictive maintenance with machine learning (ML), which has crucial economic implications because of the very high Operation and Maintenance (O&M) costs of such systems [3], [4]. We envision that different functionalities, ranging from real-time control to ML algorithms, with their diverse needs and requirements, can cohabit in the same mixedcriticality edge node platform.…”

mentioning

confidence: 99%

Virtualization over Multiprocessor System-on-Chip: an Enabling Paradigm for Industrial IoT

Cilardo,

Cinque,

De Simone

et al. 2021

Preprint

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The next-generation Industrial Internet of Things (IIoT) inherently requires smart devices featuring rich connectivity, local intelligence, and autonomous behavior. Emerging Multiprocessor System-on-Chip (MPSoC) platforms along with comprehensive support for virtualization will represent two key building blocks for smart devices in future IIoT edge infrastructures. We review representative existing solutions, highlighting the aspects that are most relevant for integration in IIoT solutions. From the analysis, we derive a reference architecture for a general virtualization-ready edge IIoT node. We then analyze the implications and benefits for a concrete use case scenario and identify the crucial research challenges to be faced to bridge the gap towards full support for virtualization-ready IIoT nodes.

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Deep Learning in Intelligent Power and Energy Systems

Mota

Pinto

Vale

et al. 2022

Intelligent Data Mining and Analysis in Power and Energy Systems

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DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms

Cited by 10 publications

References 15 publications

WITHDRAWN: RPUGuard: a Building Block for Next-Generation MPSoCs Hypervisors toward Mixed-Criticality Systems

WITHDRAWN: RPUGuard: a Building Block for Next-Generation MPSoCs Hypervisors toward Mixed-Criticality Systems

Virtualization over Multiprocessor System-on-Chip: an Enabling Paradigm for Industrial IoT

Deep Learning in Intelligent Power and Energy Systems

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