Somdip is the Chief Scientist of Nosh Technologies, an MIT Innovator Under 35 and a Professor of Practice (AI/ML) at the Woxsen University. As a leader in the artificial intelligence (AI) domain ...

Some companies are already using the lower-power ARM-M processor for embedded machine learning applications. For example, the Amiko Respiro is an inhaler for asthma patients that uses data from ...

but if you’re interested in developing your own embedded machine-learning applications, training custom models on the platform has historically been tricky due to the Pi’s limited processing ...

Machine learning in embedded systems specifically target embedded systems to gather data, learn and predict for them. These systems typically consist of low memory, low Ram and minimal resources ...

A new microcontroller claims to offer hardware-assisted machine learning (ML) acceleration for the Internet ... ModusToolbox provides a collection of development tools, libraries, and embedded runtime ...

The embedded machine learning features mean developers don’t have to extract data from another database to populate training models. Training also incurs no additional cost to customers in ...

I recently caught up with Hellerstein, who predicts machine learning will soon be commonplace -- embedded within the tools and applications used on a daily basis within data-driven businesses.

And York sees a major opportunity arising for any company that is able to develop and offer conventional and embedded versions of its software to enable a seamless transition between the two formats.

The current approach to machine learning at 84.51° emerged from an initiative called “Embedded Machine Learning” (EML). Scott Crawford leads the initiative, but it had multiple progenitors.