Realization of Random Forest for Real-Time Evaluation through Tree Framing

Buschjäger, Sebastian; Chen, Kuan-Hsun; Chen, Jian-Jia; Morik, Katharina

doi:10.1109/icdm.2018.00017

Cited by 24 publications

(21 citation statements)

References 17 publications

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“…An additional advantage of the random forest algorithm is its suitability for implementation in the prototypes of sensor nodes. It should be noted that several approaches to implementation of the random forest classifier for embedded devices are available in the literature [ 53 , 54 , 55 ]. In this study the random forest algorithm was used for activity recognition as well as for data classification to decide which data have to be transmitted.…”

Section: Methodsmentioning

confidence: 99%

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Lewandowski

Płaczek

Bernaś

2020

Sensors

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The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.

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Section: Methodsmentioning

confidence: 99%

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Lewandowski

Płaczek

Bernaś

2020

Sensors

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“…DTs with small depths are very fast classifiers, and it is convenient to obtain their C++ code for the use in embedded systems. When the classifier model is trained and is ready for operation, the tree structure and the split values of the tree from the trained model are extracted, and then converted to deployable C++ code, as proposed by Buschjäger et al [21] as a standard if-else tree. The tree then is appended as the next step in the pipeline after feature extraction.…”

Section: Materials and Methodsmentioning

confidence: 99%

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

Yayla

Toma

Chen

et al. 2019

Sensors

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A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. A biosensor called PAMONO (for Plasmon Assisted Microscopy of Nano-sized Objects) represents a viable technology for mobile real-time detection of viruses and virus-like particles. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. For analysis of the images provided by the sensor, state-of-the-art methods based on convolutional neural networks (CNNs) can achieve high accuracy. However, such computationally intensive methods may not be suitable on most mobile systems. In this work, we propose nanoparticle classification approaches based on frequency domain analysis, which are less resource-intensive. We observe that on average the classification takes 29 μs per image for the Fourier features and 17 μs for the Haar wavelet features. Although the CNN-based method scores 1–2.5 percentage points higher in classification accuracy, it takes 3370 μs per image on the same platform. With these results, we identify and explore the trade-off between resource efficiency and classification performance for nanoparticle classification of images provided by the PAMONO sensor.

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“…Here, we evaluate the execution time of LBW BNNs and compare it to regular ones using commonly available CPUs for the BNN operations. As experiment platforms for the execution time measurements of machine learning models we use the same setup as the work in [36]. Furthermore, we generate C++ code from PyTorch models with the same framework as the study in [37].…”

Section: Lbw Execution Of Bnnsmentioning

confidence: 99%

FeFET-Based Binarized Neural Networks Under Temperature-Dependent Bit Errors

Yayla

Buschjäger

Gupta

et al. 2022

IEEE Trans. Comput.

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Ferroelectric FET (FeFET) is a highly promising emerging non-volatile memory (NVM) technology, especially for binarized neural network (BNN) inference on the low-power edge. The reliability of such devices, however, inherently depends on temperature. Hence, changes in temperature during run time manifest themselves as changes in bit error rates. In this work, we reveal the temperature-dependent bit error model of FeFET memories, evaluate its effect on BNN accuracy, and propose countermeasures. We begin on the transistor level and accurately model the impact of temperature on bit error rates of FeFET. This analysis reveals temperature-dependent asymmetric bit error rates. Afterwards, on the application level, we evaluate the impact of the temperature-dependent bit errors on the accuracy of BNNs. Under such bit errors, the BNN accuracy drops to unacceptable levels when no countermeasures are employed. We propose two countermeasures: (1) Training BNNs for bit error tolerance by injecting bit flips into the BNN data, and (2) applying a bit error rate assignment algorithm (BERA) which operates in a layer-wise manner and does not inject bit flips during training. In experiments, the BNNs, to which the countermeasures are applied to, effectively tolerate temperature-dependent bit errors for the entire range of operating temperature.

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Realization of Random Forest for Real-Time Evaluation through Tree Framing

Cited by 24 publications

References 17 publications

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring

Nanoparticle Classification Using Frequency Domain Analysis on Resource-Limited Platforms

FeFET-Based Binarized Neural Networks Under Temperature-Dependent Bit Errors

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