TinyML: A bag full of opportunities!

TinyML

There are 250 billion microcontrollers in the world today. 28.1 billion units were sold in 2018 alone, and by 2023 the volume would grow to 38.2 billion by 2023 according to IC Insights forecasts.

The world is getting a lot smarter and smaller, we hear predictions on so many things from fully remote workforces to quantum computing. But there is one such emerging trend that has changed our lives forever scarcely – one that is small in form but has the potential to be huge in implication, Microcontrollers.

A microcontroller is a special purpose computer, which is smaller in size and is dedicated to performing one task or program within a device. And a microcontroller-based computer hardware system with software that is designed to perform a dedicated function, either as an independent system or as a part of a large system is called embedded systems. 

Anything, starting from our office machines, cars, medical devices, and home appliances almost all certainly have microcontrollers in them.

With all the budding technologies like cloud computing, mobile device penetration, artificial intelligence, and the Internet of Things (IoT) over the last few years, microcontrollers have largely been underappreciated. 

But it is the Microcontrollers that facilitate automation and embedded control in electronic systems, as well as the connection of sensors and applications to the IoT. The sensor data from the physical world is the reason for digital transformation in the industry. These handy little devices are also very cheap. Although low in cost, the economic impact of what microcontrollers enable at the system level is massive.

Improved hardware with more efficient development standards has made it easier to build programs on these devices. And this gave rise to a tiny machine learning, widely known asTinyML.

Big potential

TinyML engulfs the field of machine learning technologies capable of performing on-device analytics of sensor data at low power. A glance under the hood shows this is fundamentally possible because deep learning models are limited by the time it takes to complete a large number of arithmetic operations. The advancement in TinyML has made it possible to run these on existing microcontroller hardware.

In other words, those billion microcontrollers in our TVs, cars, and all other devices can now perform tasks that previously only our computers and smartphones could handle. This is the era for smarter devices and appliances, thanks to microcontrollers!

TinyML represents a combined effort between the embedded ultra-low-power systems and machine learning. This union has opened our eyes to new and exciting applications of on-device machine learning. The phrases like “Okay Google” and “Hey Siri,” are few examples of the wider picture.

But what makes it important that we can run these models on microcontrollers? Usually, the sensor data formed today is discarded because of limitations like cost, bandwidth, or power. For example, take an imagery micro-satellite, such satellites have to store images at low resolution and a low frame rate. The computing resources on these micro-satellites are too small to support image detection deep learning models, TinyML now makes this possible.

Another factor is that microcontrollers use very little energy, compared to systems that require a direct connection to the power grid or replacement of the battery. 

Applications 

TinyML is one of the most intriguing technologies that will happen to mankind, we can use them in various ways like to route traffic efficiently and reduce response times for emergency vehicles. It can also reduce downtime due to equipment failure by facilitating real-time decisions and can alert workers. Also, we can prevent items from becoming out of stock, by monitoring shelves in-store and sending instant alerts. We can develop devices that can monitor health vitals like heart rate, blood pressure, the temperature in livestock and pets, etc.

TinyML is a giant opportunity that’s just beginning to emerge and we need more machine learning experts who can make TinyML even more convenient.

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