New Thinking Required for Machine Learning Semiconductor Manufacturing & Design Community

Judging by the presentations at the 2018 Symposium on VLSI Technology, held in Honolulu this summer, the semiconductor industry has a challenge ahead of it: how to develop the special low-power hardware needed to support artificial intelligence-enabled networks. To meet society's needs for low-power-consumption machine learning (ML), "we do need to turn our attention to this new type of computing," said Naveen Verma, an associate professor of electrical engineering at Princeton University." While introducing intelligence into engineering systems has been what the semiconductor industry has been all about, Verma said machine learning represents a "quite distinct" inflection point. Accustomed as it is to fast-growing applications, machine learning is on a growth trajectory that Verma said is "unprecedented in our own industry" as ML algorithms have started to outperform human capabilities in a wide variety of fields. Faster GPUs driven by Moore's Law, and combining chips in packages by means of heterogenous computing, "won't be enough as we proceed into the future.