How did TinyML emerge as a game-changer in ML Applications?
Over the course of time man has invented a lot of tools and technologies to lead life seamlessly. Hence, their burden of everyday work becomes easier. And here we are surrounded by machine learning models to guide us through our daily chores. Presently, living in a world powered with Artificial Intelligence Technologies, Machine Learning Models and Deep Learning Algorithms has made our way of life lucid. Starting from clicking a picture, checking the weather and scrolling through your social media accounts you might not be aware of how Artificial Intelligence has become an integral part of our life.
The Inception of TinyML:
Since the inception of Artificial Intelligence Technologies, our everyday work has become easier. TinyML is another field of machine learning and embedded systems that explores several types of models that can run on low power devices such as microcontrollers.
TinyML came into the picture when machine learning technology was facing challenges of consuming high power hence, TinyML has solved the problem and enables you to run on low power consumption. It empowers you with low power, low bandwidth, low latency at edge devices. Standard consumer CPUs consume around 65 to 85 watts and a standard consumer GPU consumes around 200 watts to 500 watts. Whereas a typical microcontroller powered by TinyML consumes only around 200 watts to 500 watts.
The four advantages of using TinyML
- Less Power Consumption: TinyML has given birth to microcontrollers which consume very little power and enable them to run without being charged for a longer time.
- Reduced Latency: As TinyML models run on the age hence the data need not have to be sent to the server for running inference. This minimizes the latency of the output.
- Enhanced Privacy: As TinyML models run on edge computing the information is not stored and any service which makes it more secure.
- Low bandwidth Usage: The data used in the tiny animal does not have to be sent to the server constantly which also needs less internet bandwidth.
Applications of TinyML models in several verticals:
In healthcare, TinyML is used for eating several solar scare mosquito projects. It is used for preventing the spread of mosquito-borne diseases like malaria, dengue, zika virus, etc. TinyML models work by detecting mosquito breeding in various conditions. It agitates the water and prevents the mosquito from breeding. Moreover, these models run on solar power and hence can be used indefinitely.
Another great machine learning application that helps farmers detect disease in plants just by clicking a picture of it and running it through a machine learning model on a device making use of Tensorflow Lite. It works on the device and does not need an internet connection too. This TinyML application has helped farmers in remote areas who do not have a stable Internet connection at the place of cultivation.
- Conservation of Ocean life
Today smart machine learning power devices are utilized for monitoring whales in real-time while they are one drink in the ocean. This has helped Seattle and Vancouver to avoid whale strikes in various busy shipping lanes.
Final Thoughts on TinyML Technologies
AI Technologies and Machine Learning Algorithms have helped a lot of industries to come up with interesting solutions to their long faced challenges. And TinyML has also emerged as one of the game-changing technologies to support and evolve artificial intelligence technologies in an all-new way. Though at present there are only a few frameworks that cater to TinyML needs, it is constantly evolving. With TinyML applications, you can save a lot of dollars as well as a lot of power for the future as well.
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Originally published at https://www.datalabeler.com on December 6, 2021.