Edge AI and TinyML are two terms that you may have heard thrown around a lot lately but may not be entirely sure what they mean.
In this article, we’ll take a closer look at each term and explain what they mean in simple terms. By the end, you should have a good understanding of both Edge AI and TinyML and how they can be used to benefit your business.
Edge AI makes it possible for artificial intelligence algorithms to run locally, either on the device or on the server closest to the device.
Some of the advantages of edge AI include improved privacy, security, latency, and load balancing.
The edge AI market is forecasted to grow to $3 billion by 2030 (representing a CAGR of more than 25%).
Edge AI and TinyML
Edge AI is part of the TinyML trend.
Tiny machine learning is a technique that shrinks deep learning networks to fit into small hardware.
Global shipments of TinyML chipsets are expected to reach 2.5 billion units by 2030.
This represents a 164x increase from its 2020 levels of 15.2 million units.
FAQs – Edge AI
What is Edge AI?
Edge AI is a type of artificial intelligence (AI) that is able to run locally on devices that are not connected to the internet.
This is in contrast to traditional AI models which require data to be sent to the cloud in order for processing to take place. Edge AI has numerous benefits over traditional AI, chief among them being improved privacy and security as well as reduced latency.
How Does Edge AI Work?
Edge AI gets its name from the fact that it runs on the “edge” of networks, meaning at the point where devices connect to the internet.
This allows data to be processed locally on devices instead of having to be sent to the cloud for processing. One of the benefits of this is that it reduces latency, meaning there is less of a delay between an action being taken and a response being received.
Another benefit is that it can improve privacy and security as data never has to leave the device it is stored on.
What is TinyML?
TinyML is a type of machine learning (ML) that allows ML models to be run on very small devices such as microcontrollers.
TinyML has numerous benefits over traditional ML, chief among them being improved power efficiency as well as reduced cost and complexity.
How Does TinyML Work?
TinyML works by allowing ML models to be run directly on small devices such as microcontrollers.
This is in contrast to traditional ML models which require data to be sent to the cloud or a server in order for processing to take place.
One of the benefits of this is that it improves power efficiency as data does not have to be constantly transmitted between devices.
Another benefit is that it can reduce cost and complexity as there is no need for expensive hardware or infrastructure.
tinyML Talks: The Value of Edge AI for Industrial Applications: onsemi and SensiML IIoT Solutions
Conclusion – Edge AI and TinyML: What They Are and How They Work
Edge AI and TinyML are forms of AI and ML respectively that allow for algorithms to be run locally on devices.
The main benefits of this are improved privacy and security as well as reduced latency.
Edge AI is part of the TinyML trend which is forecasted to grow rapidly in the coming years.