6 research outputs found
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