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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 Fallon , Michael Kotelyanski , Venki Venkatesh , Anthony Yu , Nick Harris , David Graham , Thomas Doyle , Aspinity Doyle , Aspinity Graham , Keith Schaub , Infineon Pandey , Ramesh Chettuvetty , Huaiyu Meng , Ashutosh Pandey , Maurice Steinman , Sumit Vishwakarma , Group At Cadence , Field Programmable Analog Array , Tortuga Logic , Hot Chips , எலியாஸ் விழும் , அந்தோணி யூ , நிக் ஹாரிஸ் , டேவிட் கிரஹாம் , தாமஸ் டாய்ல் , அசுதோஷ் பாண்டே ,