Until now building machine learning (ML) algorithms for hardware meant complex mathematical mode s based on sample data, known as “ training data ,” in order to make predictions or decisions without being explicitly programmed to do so.

And if this sounds complex and expensive to build, it is. 

But, what if there’s another, more agile way.

The implications of TinyML accessibility are very important in today’s world. For example, a typical drug development trial takes about five years as there are potentially millions of design decisions that need to be made on route to FDA approval. Using the power of TinyML and hardware, not animals, for testing models can speed up the process and take just 12 months.

Another example of this game-changing technology in terms of building neural networks is the ability to fix problems and create new solutions for things we couldn’t dream of doing before. For example, TinyML can listen to beehives and detect anomalies and distress caused by things as small as wasps. A tiny sensor can trigger an alert based on a sound model that identifies a hive under attack, allowing farmers to secure and assist the hive, in real-time.

There are 250 billion micro-controllers in the world today. 28.1 billion units were sold in 2018 alone, and IC Insights forecasts annual shipment volume to grow to 38.2 billion by 2023.

What if they all became smart? How would that change our world?

From venturebeat.com:

TinyML broadly encapsulates the field of machine learning technologies capable of performing on-device analytics of sensor data at extremely low power. Between hardware advancements and the TinyML community’s recent innovations in machine learning, it is now possible to run increasingly complex deep learning models (the foundation of most modern artificial intelligence applications) directly on microcontrollers. A quick glance under the hood shows this is fundamentally possible because deep learning models are compute-bound, meaning their efficiency is limited by the time it takes to complete a large number of arithmetic operations. Advancements in TinyML have made it possible to run these models on existing microcontroller hardware.