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GlobalFoundries Wins AI Chips

July 28, 2020

Author: Linley Gwennap

Chip giants like Nvidia can afford 7nm technology, but startups and other smaller companies struggle with the difficult design rules and high tapeout cost, all for modest gains in transistor speed and cost. GlobalFoundries offers an alternative route with its new 12LP+ technology, which reduces power by cutting voltage rather than transistor size. It has also developed new SRAM and multiply-accumulate (MAC) circuits optimized specifically for AI acceleration. The result: typical AI operations consume up to 75% less power. Customers such as Groq and Tenstorrent are already achieving industry-leading results using the initial 12LP technology, and the first products manufactured in 12LP+ will tape out later this year.

To achieve these results, GlobalFoundries (GF) took a holistic approach to AI acceleration, specifically infer­encing convolu­tional neural networks (CNNs). This work-load relies heavily on MAC operations, but the com­pany discov­ered that most of the power actually goes to reading data from local SRAM and transferring it to the MAC units. The new SRAM design greatly decreases pow­er for CNNs and other applications that commonly access long data vectors. The new MAC design targets the smaller data types and lower clock speeds of most AI accelerators, also con­serving power. The voltage reduction comes from rede­sign­ing the paired transistors in the SRAM cell to im­prove matching, thereby shrinking the required voltage margin.

GF took this path after dropping its plan for 7nm and beyond to instead focus on FD-SOI, SiGe, and other differentiated technologies. The 12LP+ and the AI efforts are further examples of its differentiation strategy. The advantages of this approach are in some ways greater than those of 7nm but come at a lower cost. Previously, the foundry had focused on building AMD’s high-performance CPUs, but as AMD has moved them to TSMC, the revised strategy has helped GF attract new customers.

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