Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Raza, Wamiq; Osman, Anas; Ferrini, F.; Natale, Francesco G. B. De

doi:10.3390/drones5040127

Cited by 25 publications

(14 citation statements)

References 25 publications

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“…In addition, Ericsson, one of the biggest network companies, predicted that TinyML plays a platform role, such as TinyML-as-a-Service [25]. According to the benefits of TinyML in terms of energy efficiency, low cost, data security, and latency, there have been several efforts to apply TinyML to various services and applications [26][27][28][29][30][31]. Raza et al [26] leveraged TinyML to provide intelligence to unmanned aerial vehicles (UAVs) with advanced decisionmaking capabilities.…”

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

“…According to the benefits of TinyML in terms of energy efficiency, low cost, data security, and latency, there have been several efforts to apply TinyML to various services and applications [26][27][28][29][30][31]. Raza et al [26] leveraged TinyML to provide intelligence to unmanned aerial vehicles (UAVs) with advanced decisionmaking capabilities. To complete the given mission in an energy-efficient way, the MCU included in the UAV hosts a set of ML inference tools and determines the moving direction based on selflearning.…”

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

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TinyML-Based Classification in an ECG Monitoring Embedded System

Kim¹,

Kim²,

Park³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

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Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

TinyML-Based Classification in an ECG Monitoring Embedded System

Kim¹,

Kim²,

Park³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Raza et al [39] looked at using TinyML on the edge for UAVs for ML inference using a microcontroller integrated into a DJI Tello Micro Aerial Vehicle (MAV), a type of drone. They defined a mission in which a drone navigated a populated area while identifying people (face detection task) and classifying whether they were wearing a protective mask.…”

Section: Tinymlmentioning

confidence: 99%

LimitAccess: on-device TinyML based robust speech recognition and age classification

Maayah

Abunada

Al-Janahi

et al. 2023

Discov Artif Intell

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Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword “open”. The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score.

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“…There are very few recent works covering the topic of embedded systems used for face mask detection, for example, the Refs. [25][26][27]. The authors used powerful processors in order to achieve impressive results.…”

Section: Related Workmentioning

confidence: 99%

“…Platform cost comparison is shown in Table 1. In the mentioned papers [25][26][27], the researchers focus on the binary classification problem: whether the person wears a face mask or not. Our paper focuses on the classification of the correctly masked face and incorrectly masked face.…”

Section: Related Workmentioning

confidence: 99%

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

Piątkowski

Walkowiak

2022

Applied Sciences

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As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

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Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs

Cited by 25 publications

References 25 publications

TinyML-Based Classification in an ECG Monitoring Embedded System

TinyML-Based Classification in an ECG Monitoring Embedded System

LimitAccess: on-device TinyML based robust speech recognition and age classification

TinyML-Based Concept System Used to Analyze Whether the Face Mask Is Worn Properly in Battery-Operated Conditions

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