TinyML – A Giant Opportunity And Challange For Low Power ML Applications

TinyML For Low Power ML Applications – A small sized computer designed for serving particular purposes. It dedicated to performing a single program or task within the computer and known as a Microcontroller. There are about two hundred fifty billion microcontrollers in total.

They sold about 28.1 billion units of microcontrollers in the financial year of 2018. It estimated that the sales of these microcontrollers would increase with the progression of time.

TinyML – A Giant Opportunity

These microcontrollers prepared after merging several new trends of technology. These computers act as the perfect means to serve the Internet of Things apps. Also, these computers are robust processing system independently.

It recently observed that microcontrollers are now able to perform calculations faster than their earlier speed. It could be possible because of the advancement in hardware systems.

So, the merger of modified hardware and many practical standards of development have made the work easy. Now, all the developers can easily create programs on these microcontrollers.

One of the much talked about the modern technological trends known as the tiny machine learning (TML) pattern. These TinyML trends span around the field of technologies based on machine learning.

The machine learning technologies are capable of efficiently working at even lower power standards. The main task of these technologies includes the sensor data’s on-device analytics.

TinyML has recently done very progressive innovations. When these innovations incorporated with the hardware modifications, then the results are exemplary. The combination of these trends helps the microcontrollers to support sophisticated programs/ models like deep learning.

TinyML For Low Power ML Applications

The deep learning programs/models – one of the most contemporary forms of Artificial Intelligence-powered applications.

Deep learning programs/ models known as compute-bounded. They have limited efficiency. Therefore, it takes a great deal of time for these applications to perform lengthy calculations.


With the technological advancements, it is now possible to see these deep learning programs/ models perform on microcontrollers. All the microcontrollers get retrofit. Be it in the pacemakers, cars, printers or Television sets, etc. Every microcontroller is becoming advanced.

No, they are capable enough to perform complex tasks as well. Earlier, only Computers or Smartphones used to perform complex tasks like deep learning and lengthy calculations.

TinyML is the representative of the hard work of in-depth learning programs and low power systems. Both of the trends used to work independently earlier. With the merger of these technologies, several similar technologies will develop. Now, various brand new and enthralling machine learning applications developed.

Microcontrollers run the functionality of many of the voice assistants of big companies. The pattern employed for performing deep learning models on microcontrollers is responsible for these voice assistants.

Low Power ML Applications

 So, the conglomeration of microcontrollers with deep learning applications is perfect for further technological advancements as well.

TinyML For Low Power ML Applications works to make the complex tasks performed by the microcontrollers possible. That is the sole reason these microcontrollers are the primary choice to run deep learning applications. Also, the microcontrollers are energy efficient. They perform effectively, even on low levels of energy.

If a comparison made between other devices, Microcontrollers are more efficient in performing the task at hand. As for some devices, a constant power connection or a direct supply of power needed. Even in some cases battery required to change. Microcontrollers can perform the same amount of work as these devices in a lesser amount of power supply.

Most of the embedded systems do not have an active internet connection at all times. Therefore all these smart systems used at any time and anywhere. With the in-depth learning programs on these embedded systems, a new horizon for advanced technologies created.

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