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Developers Turn To Analog For Neural Nets Replacing digital with analog circuits and photonics can improve performance and power, but it’s not that simple. Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyone’s looking at the fact that deep neural networks are so energy-intensive when you implement them in digital, because you’ve got all these multiply-and-accumulates, and they’re so deep, that they can suck up enormous amounts of power,” said Elias Fallon, software engineering group director for the Custom IC & PCB Group at Cadence.

Elias fallonMichael kotelyanskiVenki venkateshAnthony yuNick harrisDavid grahamThomas doyleAspinity doyleAspinity grahamKeith schaubInfineon pandeyRamesh chettuvettyHuaiyu mengAshutosh pandeyMaurice steinmanSumit vishwakarma