Enabling Machine Learning on Resource Constrained Devices by Source Code Generation of the Learned Models

Szydło, Tomasz; Sendorek, Joanna; Brzoza-Woch, Robert

doi:10.1007/978-3-319-93701-4_54

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…The focus is to reveal the performance of various algorithms (e.g., Random Forests, Support Vector Machines, Multi-Layer Perceptron) in constrained devices. It is known that the highly regarded programming libraries consume to much resources to be ported to the embedded processors [40]. In [29], a service-provisioning framework for coalition operations is extended to address specific requirements for robustness and interpretability, allowing automatic selection of service bundles for intelligence, surveillance and reconnaissance tasks.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Karanika¹,

Oikonomou²,

Kolomvatsos³

et al. 2020

Lecture Notes in Computer Science

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Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently select the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.

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Section: Related Workmentioning

confidence: 99%

An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Karanika¹,

Oikonomou²,

Kolomvatsos³

et al. 2020

Lecture Notes in Computer Science

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“…Designing machine learning models [8,9] to embed within a IoT devices for providing fast accurate result with reduced latency for resource constrained devices. On this context few architectural designs [10] and strategies are developed for time stringent applications are discussed below.…”

Section: B Architectural Design For Fast Inferencementioning

confidence: 99%

Machine Learning at Resource Constraint Edge Device Using Bonsai Algorithm

Naveen

Kounte

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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In the worldwide billions of devices connected each other to interact with the surrounding environment to collect the data based on the context. Using machine learning algorithm intelligence can be incorporated in these Internet of Things (IoT) devices to get valuable insights from these data for accurate predictions. Machine learning model is deployed onto the devices for making the decisions locally. This enables fast, accurate prediction within few milliseconds by evading data transmission to the cloud and makes perfectly applicable for real time applications. In this paper, the experiment is conducted with publicly available dataset with Bonsai algorithm. This algorithm is implemented in Linux environment with core i5 processor in python 2.7 and achieved 92% accuracy with model size of 6.25KB, which can be easily deployed on resource constraint IoT devices.

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“…Since sensors have limited resources, they cannot directly run a Python library used previously to train ML models. In [31] we have proposed the solution that converts the ML model to source code and then compiles it into device firmware. Table 3 shows how the scikit-learn decision tree model can be transformed to C source code for an embedded microprocessor using the library FogML 1 .…”

Section: Hardware Infrastructure Analysismentioning

confidence: 99%

Machine Learning in the Internet of Things for Industry 4.0

Szydło¹,

Sendorek²,

Brzoza-Woch³

et al. 2020

Preprint

Self Cite

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Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow programming. It enables the development of reactive software with short processing and rapid response times, especially when moved to the edge of the network. This is especially important in systems that utilize online machine learning algorithms to analyze ongoing processes such as those observed in Industry 4.0. In this paper, we show that organization of such systems depends on the entire processing stack, from the hardware layer all the way to the software layer, as well as on the required response times of the IoT system. We propose a flow processing stack for such systems along with the organizational machine learning architectural patterns that enable the possibility to spread the learning and inferencing on the edge and the cloud. In the paper, we analyse what latency is introduced by communication technologies used in the IoT for cloud connectivity and how they influence the response times of the system. Finally, we are providing recommendations which machine learning patterns should be used in the IoT systems depending on the application type.

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Enabling Machine Learning on Resource Constrained Devices by Source Code Generation of the Learned Models

Cited by 13 publications

References 8 publications

An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Machine Learning at Resource Constraint Edge Device Using Bonsai Algorithm

Machine Learning in the Internet of Things for Industry 4.0

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