This blog explains what Edge AI aka tinyML is, why it is important and where you can learn more on the subject.
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Edge AI means that AI algorithms are processed locally on a hardware device. The algorithms are using data (sensor data or signals) that are created on the device.
A device using Edge AI does not need to be connected in order to work properly, it can process data and take decisions independently without a connection.
In order to use Edge AI, you need a device comprising a microprocessor and sensors.
Example: A handheld power tool is by definition on the edge of the network. The Edge AI software application that runs on a microprocessor in the power tool processes data from the power tool in real time. The Edge AI application generates results and stores the results locally on the device. After working hours the power tool connects to the internet and sends the data to the cloud for storage and further processing. One of the key properties in the example above is to have a long battery life. If the power tool would continuously stream data to the cloud, the battery would be drained in no time.
Edge AI will allow real time operations including data creation, decision and action where milliseconds matter. Real time operations is important for self-driving cars, robots and many other areas.
Reducing power consumption and thus improving battery life is super important for wearable devices.
Edge AI will reduce costs for data communication, because less data will be transmitted.
By processing data locally, you can avoid the problem with streaming and storing a lot of data to the cloud that makes you vulnerable from a privacy perspective.
Johan Malm, PhD and AI researcher at Imagimob, has written a very good blog about the more technical aspects of Edge AI, Edge AI for techies. Read it here.
Johan has also written a blog about project with Acconeer, where we developed an application for gesture-controlled headphones using radar and Edge AI. Read it here
S. Somasegar is the managing director of Madrona Venture Group, a venture capital firm that teams with technology entrepreneurs to nurture ideas from startup to market success. Read his article with his predictions for AI and Machine Learning in 2018.
An article in Forbes by Ami Gal, CEO and Co-Founder at SQream explains Edge AI in a very good way. Read his article about the cutting edge of IoT here.
In a recent report, ABI Research forecasts the global Edge AI SaaS and turnkey services to be worth USD7 billion in 2025. Imagimob is listed in the report. Read the report here
Developing embedded real-time applications is on its own one...