What is an Edge AI?
What is Edge AI / tinyML?
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 continously stream data to the cloud, the battery would be drained in no time.
Why is Edge AI important?
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 superimportant 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.
Some great articles about Edge AI for your reference
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.
Ben Dickson is an experienced software engineer and tech blogger. He contributes regularly to major tech websites such as the Next Web, PCMag.com, VentureBeat, International Business Times UK and The Huffington Post. Read his article explaining why Edge Ai is important.
Nathan Cranford is a writer at RCR Wireless News since 2017. His previous work has been published by a myriad of news outlets, including COEUS Magazine, dailyRx News, Texas Writers Journal and VETTA Magazine. Read his article on how to take AI from the cloud to the edge.
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.
A recent (July 15, 2019) 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, MarketsandMarkets forecasts the global Edge AI software market size to grow to USD1,1 billion in 2023. Imagimob is listed as a major player together with 14 other companies. Read about the report here.