Is Software and Hardware Ready for TinyML Tsunami?

By Ilene Wolff, EETimes (December 19, 2023)

Engineers working on embedding AI in edge devices who are just now doing their first machine learning project have high hurdles to overcome, but recent developments in the industry may offer encouragement.

“The education, I think, has gotten better in the last couple of years,” said Eta Compute CEO Evan Petridis. “There’s been a lot of efforts by the vendors and by others on the education side.”

Petridis, whose company is building a silicon-agnostic tool chain for edge AI, said the reality is that AI is tough and super-fast moving, and those who haven’t kept up with recent developments are at a disadvantage. There’s also a different mindset compared with other engineering disciplines, he told EE Times during a recent panel.

“I think culturally, or by training, embedded systems people think deterministically,” Petridis said. “And I come from a traditional engineering background, so I think deterministically. And you know, the AI world is rooted in data science, and it’s a probabilistic world.”

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