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AI at the Very, Very Edge

eetimes.com, Jul. 12, 2019 – 

When the TinyML group recently convened its inaugural meeting, members had to tackle a number of fundamental questions, starting with: What is TinyML?

TinyML is a community of engineers focused on how best to implement machine learning (ML) in ultra-low power systems. The first of their monthly meetings was dedicated to defining the issue. Is machine learning achievable for low power devices such as microcontrollers? And are specialist ultra-low-power machine learning processors required?

Evgeni Gousev from Qualcomm AI Research defined TinyML as machine learning (ML) approaches that consume 1mW or below. Gousev said that 1mW is the "magic number" for always-on applications in smartphones.

"There is a lot of talk about cloud ML, while ML at the smartphone level becomes more and more sophisticated," he said. "But if you look at the data, 90 percent of the data is in the real world. How do you connect all these cameras, IMUs, and other sensors and do ML at that level?"

"Tiny ML is going to be big, and there is a real, urgent need to drive the whole ecosystem of tiny ML, including applications, software, tools, algorithms, hardware, ASICs, devices, fabs, and everything else," Gousev said.

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