[2606.15870] Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations

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Computer Science > Hardware Architecture

arXiv:2606.15870 (cs)

[Submitted on 14 Jun 2026]

Title:Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations

Authors:Norman P. Jouppi, Sridhar Lakshmanamurthy, Cliff Young, David Patterson<br>View a PDF of the paper titled Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations, by Norman P. Jouppi and 3 other authors

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Abstract:This paper (to appear in the July/August 2026 issue of IEEE Micro magazine) summarizes five generations of Google s TPUs, from TPU v2 to Ironwood, highlighting their evolution as scalable, resilient, power-efficient, sustainable supercomputers for AI training. It details the TPU s stable architecture, which has surprisingly easily accommodated the rapidly changing deep neural network workloads, such as the rise of Transformers. Key advancements over eight years include 10x increase in HBM capacity and bandwidth per node, a 100x increase in peak node performance, and a 3600x increase in supercomputer performance. The paper also discusses the role of optical circuit switches, built-in self test, and hardware replay in enhancing resilience and how TPU's environmental impact is reduced with substantial improvements in performance per Watt and in carbon emissions per floating point operation. It concludes by identifying six features that may well characterize successful training accelerators of this decade.

Subjects:

Hardware Architecture (cs.AR)

ACM classes:<br>B.0; C.1.2; C.3; I.2

Cite as:<br>arXiv:2606.15870 [cs.AR]

(or<br>arXiv:2606.15870v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2606.15870

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arXiv-issued DOI via DataCite (pending registration)

Related DOI:

https://doi.org/10.1109/MM.2026.3699647

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DOI(s) linking to related resources

Submission history<br>From: Cliff Young [view email]<br>[v1]<br>Sun, 14 Jun 2026 15:44:31 UTC (2,943 KB)

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