19 citations · 24 across the 3 of their papers we have counts for
3 papers · 1 filter
Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations
Norman P. Jouppi, Sridhar Lakshmanamurthy, Cliff Young +1
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 s…
Challenges and Research Directions for Large Language Model Inference Hardware
Xiaoyu Ma, David Patterson
Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerb…
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
Norman P. Jouppi, George Kurian, Sheng Li +11
In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and it…