A review of TinyML

In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope

Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices

Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research. We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings. We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round. Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods

Hybrid-SD (H_SD): A new hybrid evaluation metric for automatic speech recognition tasks

Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H_SD), a new hybrid evaluation metric for ASR systems that takes into account both semantic correctness and error rate. To generate sentence dissimilarity scores (SD), we built a fast and lightweight SNanoBERT model using distillation techniques. Our experiments show that the SNanoBERT model is 25.9x smaller and 38.8x faster than SRoBERTa while achieving comparable results on well-known benchmarks. Hence, making it suitable for deploying with ASR models on edge devices. We also show that H_SD correlates more strongly with downstream tasks such as intent recognition and named-entity recognition (NER)

MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones

We describe a comprehensive methodology for developing user-voice personalized automatic speech recognition (ASR) models by effectively training models on mobile phones, allowing user data and models to be stored and used locally. To achieve this, we propose a resource-aware sub-model-based training approach that considers the RAM, and battery capabilities of mobile phones. By considering the evaluation metric and resource constraints of the mobile phones, we are able to perform efficient training and halt the process accordingly. To simulate real users, we use speakers with various accents. The entire on-device training and evaluation framework was then tested on various mobile phones across brands. We show that fine-tuning the models and selecting the right hyperparameter values is a trade-off between the lowest achievable performance metric, on-device training time, and memory consumption. Overall, our methodology offers a comprehensive solution for developing personalized ASR models while leveraging the capabilities of mobile phones, and balancing the need for accuracy with resource constraints.Comment: Accepted in AIMLSystems 202

PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level

In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditions. When these systems malfunction or sometimes completely fail, alerts have to be generated in real-time to make sure not only production quality is not compromised but also safety of humans and equipment is assured. In this work, we describe the construction of a smart and real-time edge-based electronic product called PreMa, which is basically a sensor for monitoring the health of a Solenoid Valve (SV). PreMa is compact, low power, easy to install, and cost effective. It has data fidelity and measurement accuracy comparable to signals captured using high end equipment. The smart solenoid sensor runs TinyML, a compact version of TensorFlow (a.k.a. TFLite) machine learning framework. While fault detection inferencing is in-situ, model training uses mobile phones to accomplish the `on-device' training. Our product evaluation shows that the sensor is able to differentiate between the distinct types of faults. These faults include: (a) Spool stuck (b) Spring failure and (c) Under voltage. Furthermore, the product provides maintenance personnel, the remaining useful life (RUL) of the SV. The RUL provides assistance to decide valve replacement or otherwise. We perform an extensive evaluation on optimizing metrics related to performance of the entire system (i.e. embedded platform and the neural network model). The proposed implementation is such that, given any electro-mechanical actuator with similar transient response to that of the SV, the system is capable of condition monitoring, hence presenting a first of its kind generic infrastructure

Comparison of stress, burnout and its association among postgraduate orthodontic and undergraduate students in India

Aim and Objectives: Stress and burnout are the wave of the present decade and dentists and dental specialists are found repeatedly on top of the charts of the more stressed occupation; however, the prevalence among orthodontic postgraduates in India has not been well researched. The present study aimed to investigate the stress and burnout levels of postgraduate students of orthodontics in India. Materials and Methods: A descriptive, cross-sectional study was conducted to evaluate stress and burnout in postgraduate students of orthodontics in India. A stratified randomized sampling method, with stratification as North, East, West, South, and central population was employed. A questionnaire format formulated by the International Stress Management Association, including Maslach burnout inventory was filled by each of these individuals. Results: A total of 284 individuals showed significance for stress and personal accomplishment (PA) (P < 0.05) whereas statistically insignificant for genders. There is statistically significance for geographical distribution to depersonalization and PA. The Pearson's correlation is positive for stress and components of burnout in postgraduates and is negative for undergraduates. Conclusion: This study was the first of its kind to explore stress, burnout, and its association among orthodontic postgraduate students and undergraduates in the country. There are significant levels of stress and burnout in both undergraduates and postgraduates. There is a statistically significant positive correlation to the components of burnout found in postgraduates. These findings may help orthodontic community in planning, management, and prevention of stress and burnout