TinyML: discover the revolutionary technique of Machine Learning

This technique has the potential to change the way we operate IoT data , the so-called Internet of Things, which connects the whole world, making industry exponents such as Google, Arduino and ARM boost TinyML.

What is TinyML?

TinyML, or Tiny Machine Learning, is nothing more than a Machine Learning technique that integrates reduced and optimized machine learning applications that, in turn, need full-stack development solutions .

In addition, the technique can be implemented in low energy systems and manages to do more with less, that is, less energy, costs and without internet connection.

Why is TinyML revolutionary in Machine Learning?

In short, the ability of this technique to build artificial neural networks that, in turn, generate previously unthinkable solutions (and in different sectors) is what is most impressive. Therefore, it is already considered, by many professionals, the best technology for performing data analysis on devices for vision, audio and movement.

Furthermore, the potential use cases are, in their own way, almost unlimited and developers are already working with TinyML to develop different types of solutions that improve the quality of life of society and professionals from the most diverse segments.

TinyML Applicability

TinyML has several applications, they are:

1. Industrial predictive maintenance

TinyML technology helps modern industrial systems that need constant monitoring by deploying intelligent microcontrollers that continuously and autonomously monitor the performance of these systems using embedded algorithms.

Furthermore, the cloud is only accessed for summary data and only when absolutely necessary.

2. Health

A great example of TinyML applicability in healthcare is the Mosquito Solar Scare created through Hackaday, an open source hardware contributor. This project has been instrumental in the fight against mosquito-borne diseases such as dengue, malaria and the Zika virus.

In short, the system consists of agitating standing water in swamps and ponds, which denies the opportunity for mosquito larvae to grow. But how does TinyML help with that?

In short, it’s simple: the water churns using small robotic platforms that operate only when needed, using analysis of sensory rain and acoustic data.

3. Agriculture

PlantVillage is an open source project whose executor is Penn State University which, in turn, has created an application driven by Artificial Intelligence known as Nuru. This application helps many African farmers in mitigating threats to cassava cultivation through analysis of sensory data in the field.

Now Nuru’s next step is to use capital ML more extensively, so better tracking data will be provided for analysis.

What are the differentials of TinyML technology in Machine Learning?

For TinyML to be considered revolutionary, it certainly has aggregator differentials. Shall we meet them?

data security

There is no need to transfer data and information to external environments, that is, data privacy is much more guaranteed.

Low energy

When it comes to the act of transferring information, there is a high demand for a large server infrastructure. As mentioned above, there is no need to transmit data to external environments, thus saving energy and resources. Consequently, the costs as well.

No connection dependency

TinyML does not need internet, that is, there is no problem using a device that depends on it and, when using it, if the connection drops and you are left empty-handed with nothing to do, as it depends on the connection.

Latency

In techniques that require data transfer, delays are noticed, since this process takes time. As in TinyML it is not necessary to transfer data, the result is instantaneous.

What are the challenges of TinyML?

Notable advances have been achieved by TinyML, but there are still limitations and challenges to be overcome. Are they:

• Device Heterogeneity: To increase adoption and awareness of TinyML, the development of a generalized framework is necessary. However, it’s not as easy as it might seem, as some devices are resource-poor;
• Process power: systems based on edge computing , also known as edge computing, which have much higher processing power and speed compared to those that do not, making the quality of data analysis, when migrated from the cloud to the device, can be harmed;
• Limited DL models: TinyML needs to optimize the training and inference of DL models to analyze data on low power devices without loss of precision and with low latency, as there is a lack of development DL models that achieve good performance with high precision and small models.

Conclusion

TinyML will be responsible for opening up a series of possibilities for applications in IoT devices . That is, in the future, we will have voice interfaces in almost everything, and those who want to work with this need to be ready now.